{ "schemaVersion": 1, "title": "Library", "description": "Articles, talks, tools, and research that have shaped François Paupier’s professional thinking.", "curator": { "name": "François Paupier", "url": "https://fpaupier.fr/" }, "items": [ { "id": "posthog-ai-products", "slug": "posthog-ai-products", "source": { "title": "38 things we've learned about building AI products", "type": "article", "canonicalUrl": "https://newsletter.posthog.com/p/38-things-weve-learned-about-building", "authors": [ { "name": "Ian Vanagas", "type": "Person", "url": "https://substack.com/@ianvph" } ], "publisher": { "name": "PostHog - build mode", "url": "https://newsletter.posthog.com" }, "datePublished": "2026-10-12", "access": "public", "schemaType": "NewsArticle", "preview": { "id": "posthog-ai-products", "sourcePage": "https://newsletter.posthog.com/p/38-things-weve-learned-about-building", "sourceImage": "https://substackcdn.com/image/fetch/$s_!oh73!,w_1200,h_675,c_fill,f_jpg,q_auto:good,fl_progressive:steep,g_auto/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F98a0b0d2-ed58-4ef4-83a4-a3719fec9228_1200x630.png", "credit": "PostHog; graphics credited in article to Lottie and Daniel", "sourceWidth": 1200, "sourceHeight": 630, "width": 1200, "height": 630, "path": "/img/library/posthog-ai-products.webp", "thumbnail": { "path": "/img/library/posthog-ai-products-small.webp", "width": 480, "height": 252 } } }, "summary": "Practical lessons from building AI products at PostHog.", "curation": { "note": "I kept this for its practical, no-nonsense insights into building AI products.", "listingNote": "Practical, no-nonsense insights from building AI products." }, "topics": [ "AI & machine learning", "Product & design" ], "tags": [ "AI products", "Product engineering", "Agents" ], "related": [ { "id": "applied-llms", "reason": "Compare PostHog’s product lessons with the experience of six other LLM practitioners." }, { "id": "ai-product-evals", "reason": "Turn practical product lessons into checks based on real model outputs." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/posthog-ai-products", "status": "curated" }, { "id": "sales-playbook", "slug": "sales-playbook", "source": { "title": "The Sales Playbook For Founders | Startup School", "type": "video", "canonicalUrl": "https://www.youtube.com/watch?v=DH7REvnQ1y4", "authors": [ { "name": "Tom Blomfield", "type": "Person", "role": "Presenter" } ], "publisher": { "name": "Y Combinator", "url": "https://www.youtube.com/@ycombinator" }, "datePublished": "2025-08-15", "access": "public", "schemaType": "VideoObject", "preview": { "id": "sales-playbook", "sourcePage": "https://www.youtube.com/watch?v=DH7REvnQ1y4", "sourceImage": "https://i.ytimg.com/vi/DH7REvnQ1y4/maxresdefault.jpg", "credit": "Y Combinator / YouTube", "sourceWidth": 1280, "sourceHeight": 720, "width": 1200, "height": 675, "path": "/img/library/sales-playbook.webp", "thumbnail": { "path": "/img/library/sales-playbook-small.webp", "width": 480, "height": 270 } }, "duration": "PT18M49S", "durationDisplay": "18 min 49 s" }, "summary": "Tom Blomfield presents a process for early B2B sales.", "curation": { "note": null, "listingNote": "Tom Blomfield’s playbook for early B2B sales." }, "topics": [ "Company building" ], "tags": [ "Founder sales", "Go-to-market", "Startups" ], "related": [ { "id": "genai-needs-pricing-strategies-to-match-its-potential", "reason": "Connect founder-led sales with the problem of pricing a generative-AI product." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/sales-playbook", "status": "curated" }, { "id": "pgmq", "slug": "pgmq", "source": { "title": "Postgres Message Queue (PGMQ)", "type": "repository", "canonicalUrl": "https://github.com/pgmq/pgmq", "authors": [ { "name": "pgmq", "type": "Organization", "role": "Repository maintainer", "url": "https://github.com/pgmq" } ], "publisher": { "name": "GitHub", "url": "https://github.com" }, "datePublished": null, "access": "public", "schemaType": "SoftwareSourceCode", "preview": { "id": "pgmq", "sourcePage": "https://github.com/pgmq/pgmq", "sourceImage": "https://repository-images.githubusercontent.com/670192969/1aa21865-0a08-474a-af92-859ed458376e", "credit": "pgmq/pgmq on GitHub", "sourceWidth": 556, "sourceHeight": 234, "width": 556, "height": 234, "path": "/img/library/pgmq.webp", "thumbnail": { "path": "/img/library/pgmq-small.webp", "width": 480, "height": 202 } }, "documentationUrl": "https://pgmq.github.io/pgmq/" }, "summary": "A Postgres message queue with SQL objects, message visibility timeouts and explicit archival support.", "curation": { "note": "I was struck by how readily PGMQ adds a message queue to Postgres, in the spirit of Amazon SQS - and that it is supported in Supabase.", "listingNote": "Message queues, directly in Postgres. Also supported by Supabase." }, "topics": [ "Engineering" ], "tags": [ "Postgres", "Infrastructure", "Queues" ], "related": [ { "id": "pgvector-pgvector", "reason": "Another PostgreSQL extension that adds application infrastructure to the existing database." }, { "id": "data-intensive-applications", "reason": "The broader storage and stream-processing trade-offs behind a database-backed queue." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/pgmq", "status": "curated" }, { "id": "checklist-design", "slug": "checklist-design", "source": { "title": "Checklist Design", "type": "website", "canonicalUrl": "https://www.checklist.design/", "authors": [ { "name": "George Hatzis", "type": "Person" } ], "publisher": { "name": "Checklist Design", "url": "https://www.checklist.design/" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "sourcePage": "https://www.checklist.design/", "sourceImage": null, "credit": "Checklist Design / George Hatzis", "width": 1200, "height": 833, "path": "/img/library/checklist-design.webp", "kind": "screenshot", "capturedAt": "2026-10-13", "thumbnail": { "path": "/img/library/checklist-design-small.webp", "width": 480, "height": 333 } } }, "summary": "A library of checklists for product pages, components and flows.", "curation": { "note": "A useful collection of the functional details to consider in each part of a product. It is called Checklist Design, but its value goes beyond visual design.", "listingNote": "A useful checklist of product details, extending well beyond visual design." }, "topics": [ "Product & design" ], "tags": [ "UX", "Checklists", "Product design" ], "related": [ { "id": "your-success-with-generative-ai-may-come-down-to-these-ux-decisions", "reason": "Apply the checklist approach to interface decisions specific to generative-AI products." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/checklist-design", "status": "curated" }, { "id": "anthropic-threat-report", "slug": "anthropic-threat-report", "source": { "title": "Detecting and countering misuse of AI: September 2026", "type": "report", "canonicalUrl": "https://www.anthropic.com/threat-intelligence-report-september-2026", "authors": [ { "name": "Anthropic", "type": "Organization", "role": "Corporate author" } ], "publisher": { "name": "Anthropic", "url": "https://www.anthropic.com" }, "datePublished": "2026-09-10", "access": "public", "schemaType": "Report", "preview": { "id": "anthropic-threat-report", "sourcePage": "https://www.anthropic.com/threat-intelligence-report-september-2026", "sourceImage": "https://www-cdn.anthropic.com/images/4zrzovbb/website/7a4426f8ffe57e7de23ff36906fb1cc3efe2a82b-1200x630.jpg", "credit": "Anthropic", "sourceWidth": 1200, "sourceHeight": 630, "width": 1200, "height": 630, "path": "/img/library/anthropic-threat-report.webp", "thumbnail": { "path": "/img/library/anthropic-threat-report-small.webp", "width": 480, "height": 252 } }, "sourceSectionUrl": "https://www.anthropic.com/threat-intelligence-report-september-2026#surveillance-operations-sep-26", "pdfUrl": "https://www-cdn.anthropic.com/e50be2e51e7695dc4b1366a37a245a597377d3b5/Anthropic-Detecting-and-countering-091026.pdf" }, "summary": "Anthropic’s report on detected and disrupted AI misuse, including surveillance operations.", "curation": { "note": "I kept this for the richness of the specific misuse cases Anthropic identified and addressed.", "listingNote": "Specific, documented examples of AI misuse and how they were addressed." }, "topics": [ "AI & machine learning", "Security & defense" ], "tags": [ "AI safety", "Threat intelligence", "Surveillance" ], "related": [ { "id": "forewarned-is-forearmed-a-survey-on-large-language-model-based-agents-in-autonomous-c", "reason": "Compare observed misuse with research on autonomous attacks using language-model agents." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/anthropic-threat-report", "status": "curated" }, { "id": "europe-big-tech", "slug": "europe-big-tech", "source": { "title": "The Tech Industry Is Huge - and Europe’s Share of It Is Very Small", "type": "article", "canonicalUrl": "https://www.wsj.com/tech/europe-big-tech-ai-1f3f862c", "authors": [ { "name": "Tom Fairless", "type": "Person" }, { "name": "David Luhnow", "type": "Person" } ], "publisher": { "name": "The Wall Street Journal", "url": "https://www.wsj.com" }, "datePublished": null, "access": "restricted", "schemaType": "Article", "preview": { "id": "europe-big-tech", "sourcePage": "https://www.wsj.com/tech/europe-big-tech-ai-1f3f862c", "sourceImage": "https://images.wsj.net/im-85404807/?height=1280&width=1280", "credit": "Nadja Wohlleben for The Wall Street Journal", "sourceWidth": 1280, "sourceHeight": 1280, "width": 1200, "height": 1200, "path": "/img/library/europe-big-tech.webp", "thumbnail": { "path": "/img/library/europe-big-tech-small.webp", "width": 480, "height": 480 } } }, "summary": "Reporting on Europe’s technology industry.", "curation": { "note": "I kept this for the trajectory of Jina AI: starting as a framework, then pivoting to selling the models themselves in the search space.", "listingNote": "Jina AI’s path from a framework to selling models for search." }, "topics": [ "Company building", "AI & machine learning" ], "tags": [ "AI business", "Search", "Company strategy" ], "related": [ { "id": "la-start-up-francaise-mistral-ai-leve-105-millions-d-euros-pour-developper-des-modele", "reason": "A concrete European AI company’s early financing and model-development plans." }, { "id": "semiconductor-resilience", "reason": "Examine industrial concentration through another part of the technology supply chain." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/europe-big-tech", "status": "curated" }, { "id": "bravest-defense-plan", "slug": "bravest-defense-plan", "source": { "title": "The Bravest Defense Plan in Europe", "type": "article", "canonicalUrl": "https://defensebrief.substack.com/p/the-bravest-defense-plan-in-europe", "authors": [ { "name": "Louise Boucher", "type": "Person", "url": "https://substack.com/@louiseboucher" } ], "publisher": { "name": "The Defense Brief", "url": "https://defensebrief.substack.com" }, "datePublished": "2026-09-16", "access": "public", "schemaType": "NewsArticle", "preview": { "id": "bravest-defense-plan", "sourcePage": "https://defensebrief.substack.com/p/the-bravest-defense-plan-in-europe", "sourceImage": "https://substack-post-media.s3.amazonaws.com/public/images/88cb09e3-3fe6-4bb6-ba5d-6524e87e7810_1600x1545.png", "credit": "The Defense Brief / Louise Boucher; chart cites UK Ministry of Defence, The Defence Investment Plan, 2026, Table 24", "sourceWidth": 1600, "sourceHeight": 1545, "width": 1200, "height": 1159, "path": "/img/library/bravest-defense-plan.webp", "thumbnail": { "path": "/img/library/bravest-defense-plan-small.webp", "width": 480, "height": 464 } } }, "summary": "Commentary on Britain’s defence investment plan and leaving room for future technology.", "curation": { "note": "What interested me was the UK Ministry of Defence’s approach: leaving future funding unallocated to make room for technology that has not yet been developed.", "listingNote": "Leaving room in a defence plan for technology that does not exist yet." }, "topics": [ "Security & defense", "Company building" ], "tags": [ "Strategy", "Defense", "Technology adoption" ], "related": [ { "id": "autonomy-in-weapon-systems-dod-directive-3000-09", "reason": "Compare future defense investment with policy for governing autonomous weapon systems." }, { "id": "la-strategie-indopacifique-de-la-france", "reason": "A second national strategy connecting defense priorities with technological dependencies." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/bravest-defense-plan", "status": "curated" }, { "id": "hidden-technical-debt", "slug": "hidden-technical-debt", "source": { "title": "Hidden Technical Debt in Machine Learning Systems", "type": "paper", "canonicalUrl": "https://proceedings.neurips.cc/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.html", "authors": [ { "name": "D. Sculley", "type": "Person" }, { "name": "Gary Holt", "type": "Person" }, { "name": "Daniel Golovin", "type": "Person" }, { "name": "Eugene Davydov", "type": "Person" }, { "name": "Todd Phillips", "type": "Person" }, { "name": "Dietmar Ebner", "type": "Person" }, { "name": "Vinay Chaudhary", "type": "Person" }, { "name": "Michael Young", "type": "Person" }, { "name": "Jean-François Crespo", "type": "Person" }, { "name": "Dan Dennison", "type": "Person" } ], "publisher": { "name": "NeurIPS", "url": "https://proceedings.neurips.cc" }, "datePublished": null, "access": "public", "schemaType": "ScholarlyArticle", "publicationYear": 2015, "pdfUrl": "https://proceedings.neurips.cc/paper_files/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf", "preview": { "id": "hidden-technical-debt", "sourcePage": "https://proceedings.neurips.cc/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.html", "sourceImage": "https://proceedings.neurips.cc/paper_files/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf", "credit": "NeurIPS", "sourceWidth": 1237, "sourceHeight": 1600, "width": 1200, "height": 1552, "path": "/img/library/hidden-technical-debt.webp", "thumbnail": { "path": "/img/library/hidden-technical-debt-small.webp", "width": 480, "height": 621 }, "alt": "First page of Hidden Technical Debt in Machine Learning Systems.", "kind": "paper-first-page" } }, "summary": "A study of the maintenance costs that emerge around machine learning systems, including data dependencies, feedback loops and tightly coupled components.", "curation": { "note": null, "listingNote": "The long-term engineering costs around a machine learning model." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Technical debt", "MLOps", "Architecture" ], "related": [ { "id": "building-a-generative-ai-platform", "reason": "Connect hidden ML-system dependencies with the architecture of a generative-AI platform." }, { "id": "understanding-and-mitigating-model-drift-in-machine-learning", "reason": "Model drift is one operational source of ongoing maintenance work." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/hidden-technical-debt", "status": "curated", "citedIn": [ { "title": "GenAI : Industrialiser sans compromettre, Guide du Build vs Buy", "url": "https://fpaupier.fr/202501_productionize_genai" } ] }, { "id": "ai-engineering-flywheel", "slug": "ai-engineering-flywheel", "source": { "title": "Data Flywheels for LLM Applications", "type": "article", "canonicalUrl": "https://www.sh-reya.com/blog/ai-engineering-flywheel/", "authors": [ { "name": "Shreya Shankar", "type": "Person" } ], "publisher": { "name": "Shreya Shankar", "url": "https://www.sh-reya.com/" }, "datePublished": "2024-07-01", "access": "public", "schemaType": "Article", "preview": { "id": "ai-engineering-flywheel", "sourcePage": "https://www.sh-reya.com/blog/ai-engineering-flywheel/", "sourceImage": "https://www.sh-reya.com/blogimages/data-flywheel-pipeline.png", "credit": "Shreya Shankar", "sourceWidth": 1708, "sourceHeight": 833, "width": 1200, "height": 585, "path": "/img/library/ai-engineering-flywheel.webp", "thumbnail": { "path": "/img/library/ai-engineering-flywheel-small.webp", "width": 480, "height": 234 }, "alt": "Original preview for Data Flywheels for LLM Applications." } }, "summary": "A framework connecting task-specific evaluation, production monitoring and ongoing improvement of LLM applications.", "curation": { "note": null, "listingNote": "Turn production examples into better evaluations and better applications." }, "topics": [ "AI & machine learning", "Product & design" ], "tags": [ "Evaluation", "Monitoring", "Product iteration" ], "related": [ { "id": "ai-product-evals", "reason": "Build task-specific evaluations that make a production improvement loop useful." }, { "id": "user-intent-and-implicit-feedback-in-ai-agents-what-every-enterprise-leader-needs-to", "reason": "User feedback supplies another input to the cycle of evaluation and improvement." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/ai-engineering-flywheel", "status": "curated", "citedIn": [ { "title": "REX: GenAI in prod", "url": "https://fpaupier.fr/202409_genai_in_prod" }, { "title": "Building with Claude Code: Managing two context windows", "url": "https://fpaupier.fr/202604_claude_builders_night" } ] }, { "id": "applied-llms", "slug": "applied-llms", "source": { "title": "What We’ve Learned From A Year of Building with LLMs", "type": "article", "canonicalUrl": "https://applied-llms.org/", "authors": [ { "name": "Eugene Yan", "type": "Person" }, { "name": "Bryan Bischof", "type": "Person" }, { "name": "Charles Frye", "type": "Person" }, { "name": "Hamel Husain", "type": "Person" }, { "name": "Jason Liu", "type": "Person" }, { "name": "Shreya Shankar", "type": "Person" } ], "publisher": { "name": "Applied LLMs", "url": "https://applied-llms.org/" }, "datePublished": "2024-06-08", "access": "public", "schemaType": "Article", "preview": { "id": "applied-llms", "sourcePage": "https://applied-llms.org/", "sourceImage": "https://applied-llms.org/images/hamel_cover_img.png", "credit": "Applied LLMs", "sourceWidth": 960, "sourceHeight": 540, "width": 960, "height": 540, "path": "/img/library/applied-llms.webp", "thumbnail": { "path": "/img/library/applied-llms-small.webp", "width": 480, "height": 270 }, "alt": "Original preview for What We’ve Learned From A Year of Building with LLMs." } }, "summary": "Lessons from six practitioners on prompting, retrieval, evaluation, team practices and the economics of delivering LLM products.", "curation": { "note": null, "listingNote": "Practical lessons across the technical and organizational sides of LLM delivery." }, "topics": [ "AI & machine learning", "Product & design", "Engineering" ], "tags": [ "LLM applications", "RAG", "Evaluation" ], "related": [ { "id": "posthog-ai-products", "reason": "Compare cross-company experience with PostHog’s lessons from shipping AI products." }, { "id": "building-a-generative-ai-platform", "reason": "Follow the practical lessons into platform architecture and implementation choices." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/applied-llms", "status": "curated", "citedIn": [ { "title": "REX: GenAI in prod", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "pagedattention", "slug": "pagedattention", "source": { "title": "Efficient Memory Management for Large Language Model Serving with PagedAttention", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2309.06180v1", "authors": [ { "name": "Woosuk Kwon", "type": "Person" }, { "name": "Zhuohan Li", "type": "Person" }, { "name": "Siyuan Zhuang", "type": "Person" }, { "name": "Ying Sheng", "type": "Person" }, { "name": "Lianmin Zheng", "type": "Person" }, { "name": "Cody Hao Yu", "type": "Person" }, { "name": "Joseph E. Gonzalez", "type": "Person" }, { "name": "Hao Zhang", "type": "Person" }, { "name": "Ion Stoica", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2023-09-12", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2309.06180v1", "version": "arXiv v1", "dateModified": "2023-09-12", "identifier": "arXiv:2309.06180v1", "preview": { "id": "pagedattention", "sourcePage": "https://arxiv.org/abs/2309.06180v1", "sourceImage": "https://arxiv.org/pdf/2309.06180v1", "credit": "arXiv", "sourceWidth": 1237, "sourceHeight": 1600, "width": 1200, "height": 1552, "path": "/img/library/pagedattention.webp", "thumbnail": { "path": "/img/library/pagedattention-small.webp", "width": 480, "height": 621 }, "alt": "First page of Efficient Memory Management for Large Language Model Serving with PagedAttention.", "kind": "paper-first-page" } }, "summary": "The vLLM paper introduces PagedAttention, using ideas from virtual memory to manage the key-value cache and improve LLM serving throughput.", "curation": { "note": null, "listingNote": "The memory-management ideas behind vLLM and PagedAttention." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Inference", "vLLM", "Memory management" ], "related": [ { "id": "transformers-kv-caching-explained", "reason": "KV caching explains the attention state that PagedAttention manages." }, { "id": "vllm-easy-fast-and-cheap-llm-serving-with-pagedattention", "reason": "The project announcement connects the paper’s memory-management ideas to vLLM serving." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/pagedattention", "status": "curated" }, { "id": "rag-vs-graphrag", "slug": "rag-vs-graphrag", "source": { "title": "RAG vs. GraphRAG: A Systematic Evaluation and Key Insights", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2502.11371", "authors": [ { "name": "Haoyu Han", "type": "Person" }, { "name": "Li Ma", "type": "Person" }, { "name": "Yu Wang", "type": "Person" }, { "name": "Harry Shomer", "type": "Person" }, { "name": "Yongjia Lei", "type": "Person" }, { "name": "Zhisheng Qi", "type": "Person" }, { "name": "Kai Guo", "type": "Person" }, { "name": "Zhigang Hua", "type": "Person" }, { "name": "Bo Long", "type": "Person" }, { "name": "Hui Liu", "type": "Person" }, { "name": "Charu C. Aggarwal", "type": "Person" }, { "name": "Jiliang Tang", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2025-02-17", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2502.11371", "identifier": "arXiv:2502.11371", "preview": { "id": "rag-vs-graphrag", "sourcePage": "https://arxiv.org/abs/2502.11371", "sourceImage": "https://arxiv.org/pdf/2502.11371", "credit": "arXiv", "sourceWidth": 1237, "sourceHeight": 1600, "width": 1200, "height": 1552, "path": "/img/library/rag-vs-graphrag.webp", "thumbnail": { "path": "/img/library/rag-vs-graphrag-small.webp", "width": 480, "height": 621 }, "alt": "First page of RAG vs. GraphRAG: A Systematic Evaluation and Key Insights.", "kind": "paper-first-page" } }, "summary": "A comparative evaluation of RAG and GraphRAG for question answering and summarization, examining where graph structure helps and what trade-offs it adds.", "curation": { "note": null, "listingNote": "Evidence for choosing between text retrieval and graph-based retrieval." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "RAG", "GraphRAG", "Retrieval" ], "related": [ { "id": "from-local-to-global-a-graph-rag-approach-to-query-focused-summarization", "reason": "Read the original GraphRAG approach alongside this comparison with conventional RAG." }, { "id": "knowledge-graph-deep-learning-a-case-study-in-question-answering-in-aviation-safety-d", "reason": "A domain-specific example of combining knowledge graphs and question answering." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/rag-vs-graphrag", "status": "curated" }, { "id": "llm-as-judge", "slug": "llm-as-judge", "source": { "title": "Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2306.05685v4", "authors": [ { "name": "Lianmin Zheng", "type": "Person" }, { "name": "Wei-Lin Chiang", "type": "Person" }, { "name": "Ying Sheng", "type": "Person" }, { "name": "Siyuan Zhuang", "type": "Person" }, { "name": "Zhanghao Wu", "type": "Person" }, { "name": "Yonghao Zhuang", "type": "Person" }, { "name": "Zi Lin", "type": "Person" }, { "name": "Zhuohan Li", "type": "Person" }, { "name": "Dacheng Li", "type": "Person" }, { "name": "Eric P. Xing", "type": "Person" }, { "name": "Hao Zhang", "type": "Person" }, { "name": "Joseph E. Gonzalez", "type": "Person" }, { "name": "Ion Stoica", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2023-06-09", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2306.05685v4", "version": "arXiv v4", "dateModified": "2023-12-24", "identifier": "arXiv:2306.05685v4", "preview": { "id": "llm-as-judge", "sourcePage": "https://arxiv.org/abs/2306.05685v4", "sourceImage": "https://arxiv.org/pdf/2306.05685v4", "credit": "arXiv", "sourceWidth": 1237, "sourceHeight": 1600, "width": 1200, "height": 1552, "path": "/img/library/llm-as-judge.webp", "thumbnail": { "path": "/img/library/llm-as-judge-small.webp", "width": 480, "height": 621 }, "alt": "First page of Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.", "kind": "paper-first-page" } }, "summary": "An evaluation of LLM judges through MT-Bench and Chatbot Arena, studying agreement with human preferences alongside position, verbosity and other biases.", "curation": { "note": null, "listingNote": "What model-based evaluation can measure, and where it can mislead." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Evaluation", "LLM judges", "Benchmarks" ], "related": [ { "id": "style-outweighs-substance-failure-modes-of-llm-judges-in-alignment-benchmarking", "reason": "A focused study of how presentation style can distort language-model judgments." }, { "id": "systematic-evaluation-of-llm-as-a-judge-in-llm-alignment-tasks-explainable-metrics-an", "reason": "Broaden the comparison of LLM judges across alignment tasks and prompt templates." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/llm-as-judge", "status": "curated" }, { "id": "contextcite", "slug": "contextcite", "source": { "title": "ContextCite: Attributing Model Generation to Context", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2409.00729v2", "authors": [ { "name": "Benjamin Cohen-Wang", "type": "Person" }, { "name": "Harshay Shah", "type": "Person" }, { "name": "Kristian Georgiev", "type": "Person" }, { "name": "Aleksander Madry", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2024-09-01", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2409.00729v2", "version": "arXiv v2", "dateModified": "2024-09-13", "identifier": "arXiv:2409.00729v2", "preview": { "id": "contextcite", "sourcePage": "https://arxiv.org/abs/2409.00729v2", "sourceImage": "https://arxiv.org/pdf/2409.00729v2", "credit": "arXiv", "sourceWidth": 1237, "sourceHeight": 1600, "width": 1200, "height": 1552, "path": "/img/library/contextcite.webp", "thumbnail": { "path": "/img/library/contextcite-small.webp", "width": 480, "height": 621 }, "alt": "First page of ContextCite: Attributing Model Generation to Context.", "kind": "paper-first-page" } }, "summary": "A method for identifying which parts of a supplied context contributed to a model’s generated statements, with applications to verification and detecting poisoned context.", "curation": { "note": null, "listingNote": "Trace generated statements back to the context that influenced them." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Attribution", "Grounding", "Evaluation" ], "related": [ { "id": "extrinsic-hallucinations-in-llms", "reason": "Place context attribution within the broader problem of hallucination and verification." }, { "id": "the-rag-triad", "reason": "Groundedness evaluation offers another way to check whether answers follow supplied context." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/contextcite", "status": "curated" }, { "id": "ai-product-evals", "slug": "ai-product-evals", "source": { "title": "Your AI Product Needs Evals", "type": "article", "canonicalUrl": "https://hamel.dev/blog/posts/evals/", "authors": [ { "name": "Hamel Husain", "type": "Person" } ], "publisher": { "name": "Hamel’s Blog", "url": "https://hamel.dev/" }, "datePublished": "2024-03-29", "access": "public", "schemaType": "Article", "preview": { "id": "ai-product-evals", "sourcePage": "https://hamel.dev/blog/posts/evals/", "sourceImage": "https://hamel.dev/blog/posts/evals/images/diagram-cover.png", "credit": "Hamel’s Blog", "sourceWidth": 2081, "sourceHeight": 1109, "width": 1200, "height": 640, "path": "/img/library/ai-product-evals.webp", "thumbnail": { "path": "/img/library/ai-product-evals-small.webp", "width": 480, "height": 256 }, "alt": "Original preview for Your AI Product Needs Evals." } }, "summary": "A practical approach to evaluating AI applications through inspection of real outputs, error analysis and iterative development of task-specific checks.", "curation": { "note": null, "listingNote": "Build evaluation around the errors that appear in real product use." }, "topics": [ "AI & machine learning", "Product & design" ], "tags": [ "Evaluation", "Error analysis", "AI products" ], "related": [ { "id": "carrying-out-error-analysis", "reason": "Error analysis provides a practical foundation for choosing useful evaluation checks." }, { "id": "ai-engineering-flywheel", "reason": "Connect those checks to monitoring and continuous improvement in production." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/ai-product-evals", "status": "curated" }, { "id": "opening-up-chatgpt", "slug": "opening-up-chatgpt", "source": { "title": "Opening up ChatGPT: tracking openness of instruction-tuned LLMs", "type": "website", "canonicalUrl": "https://opening-up-chatgpt.github.io/", "authors": [ { "name": "Andreas Liesenfeld", "type": "Person" }, { "name": "Alianda Lopez", "type": "Person" }, { "name": "Mark Dingemanse", "type": "Person" } ], "publisher": { "name": "Opening up ChatGPT", "url": "https://opening-up-chatgpt.github.io/" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "documentationUrl": "https://osai-index.eu/", "preview": { "id": "opening-up-chatgpt", "sourcePage": "https://opening-up-chatgpt.github.io/", "sourceImage": "https://opening-up-chatgpt.github.io/", "credit": "Opening up ChatGPT website, captured 13 October 2026", "sourceWidth": 1440, "sourceHeight": 960, "width": 1200, "height": 800, "path": "/img/library/opening-up-chatgpt.webp", "thumbnail": { "path": "/img/library/opening-up-chatgpt-small.webp", "width": 480, "height": 320 }, "alt": "Opening up ChatGPT’s historical leaderboard and its notice directing readers to the European Open Source AI Index." } }, "summary": "A historical comparison of model openness across code, training data, weights, documentation and access. The project is no longer updated and points readers to the European Open Source AI Index.", "curation": { "note": null, "listingNote": "A multidimensional view of what “open” means for an AI model." }, "topics": [ "AI & machine learning", "Strategy & sovereignty" ], "tags": [ "Open source", "Transparency", "Model selection" ], "related": [ { "id": "opening-up-chatgpt-tracking-openness-transparency-and-accountability-in-instruction-t", "reason": "The research paper behind this openness-comparison project." }, { "id": "european-open-source-ai-index", "reason": "The successor index continues the assessment of open AI systems." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/opening-up-chatgpt", "status": "curated" }, { "id": "model-cards", "slug": "model-cards", "source": { "title": "Model Cards for Model Reporting", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/1810.03993v2", "authors": [ { "name": "Margaret Mitchell", "type": "Person" }, { "name": "Simone Wu", "type": "Person" }, { "name": "Andrew Zaldivar", "type": "Person" }, { "name": "Parker Barnes", "type": "Person" }, { "name": "Lucy Vasserman", "type": "Person" }, { "name": "Ben Hutchinson", "type": "Person" }, { "name": "Elena Spitzer", "type": "Person" }, { "name": "Inioluwa Deborah Raji", "type": "Person" }, { "name": "Timnit Gebru", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2018-10-05", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/1810.03993v2", "version": "arXiv v2", "dateModified": "2019-01-14", "identifier": "arXiv:1810.03993v2", "preview": { "id": "model-cards", "sourcePage": "https://arxiv.org/abs/1810.03993v2", "sourceImage": "https://arxiv.org/pdf/1810.03993v2", "credit": "arXiv", "sourceWidth": 1237, "sourceHeight": 1600, "width": 1200, "height": 1552, "path": "/img/library/model-cards.webp", "thumbnail": { "path": "/img/library/model-cards-small.webp", "width": 480, "height": 621 }, "alt": "First page of Model Cards for Model Reporting.", "kind": "paper-first-page" } }, "summary": "A proposal for documenting a trained model’s intended use, evaluation conditions, performance across groups and limitations through a structured model card.", "curation": { "note": null, "listingNote": "Document a model’s capabilities, limitations and intended uses." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Responsible AI", "Documentation", "Model cards" ], "related": [ { "id": "mistralai-mistral-small-3-1-24b-instruct-2503", "reason": "See a published model card for a concrete instruction-tuned model." }, { "id": "opening-up-chatgpt", "reason": "Compare model documentation with a broader assessment of openness and access." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/model-cards", "status": "curated", "citedIn": [ { "title": "Large Language Models : Souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "deepsearch-deepresearch", "slug": "deepsearch-deepresearch", "source": { "title": "A Practical Guide to Implementing DeepSearch/DeepResearch", "type": "article", "canonicalUrl": "https://jina.ai/news/a-practical-guide-to-implementing-deepsearch-deepresearch/", "authors": [ { "name": "Han Xiao", "type": "Person" } ], "publisher": { "name": "Jina AI", "url": "https://jina.ai" }, "datePublished": "2025-02-25", "access": "public", "schemaType": "Article", "preview": { "id": "deepsearch-deepresearch", "sourcePage": "https://jina.ai/news/a-practical-guide-to-implementing-deepsearch-deepresearch/", "sourceImage": "https://jina.ai/blog-banner/a-practical-guide-to-implementing-deepsearch-deepresearch.webp", "credit": "Jina AI", "sourceWidth": 1200, "sourceHeight": 630, "width": 1200, "height": 630, "path": "/img/library/deepsearch-deepresearch.webp", "thumbnail": { "path": "/img/library/deepsearch-deepresearch-small.webp", "width": 480, "height": 252 }, "alt": "Original preview for A Practical Guide to Implementing DeepSearch/DeepResearch." } }, "summary": "An implementation guide to iterative search, reading and reasoning, including stopping conditions, token budgets and the distinction between finding answers and assembling reports.", "curation": { "note": null, "listingNote": "How to build a search agent that iterates, checks its work and knows when to stop." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Agents", "Search", "Deep research" ], "related": [ { "id": "introducing-deep-research", "reason": "Compare an implementation guide with OpenAI’s research-agent product." }, { "id": "surveyx-academic-survey-automation-via-large-language-models", "reason": "Academic survey generation is a related application of iterative research and synthesis." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/deepsearch-deepresearch", "status": "curated" }, { "id": "transformers-visual", "slug": "transformers-visual", "source": { "title": "Transformers, the tech behind LLMs | Deep Learning Chapter 5", "type": "video", "canonicalUrl": "https://www.youtube.com/watch?v=wjZofJX0v4M", "authors": [ { "name": "3Blue1Brown", "type": "Organization" } ], "publisher": { "name": "3Blue1Brown", "url": "https://www.youtube.com/@3blue1brown" }, "datePublished": "2024-04-01", "access": "public", "schemaType": "VideoObject", "duration": "PT27M14S", "durationDisplay": "27 min 14 s", "preview": { "id": "transformers-visual", "sourcePage": "https://www.youtube.com/watch?v=wjZofJX0v4M", "sourceImage": "https://i.ytimg.com/vi/wjZofJX0v4M/maxresdefault.jpg", "credit": "3Blue1Brown", "sourceWidth": 1280, "sourceHeight": 720, "width": 1200, "height": 675, "path": "/img/library/transformers-visual.webp", "thumbnail": { "path": "/img/library/transformers-visual-small.webp", "width": 480, "height": 270 }, "alt": "Original preview for Transformers, the tech behind LLMs | Deep Learning Chapter 5." } }, "summary": "A visual introduction to transformer language models, tracing token representations and the computations that lead to next-token predictions.", "curation": { "note": null, "listingNote": "A visual explanation of the transformer architecture behind language models." }, "topics": [ "AI & machine learning" ], "tags": [ "Transformers", "Neural networks", "Visual explanation" ], "related": [ { "id": "attention-is-all-you-need", "reason": "The original Transformer paper provides the formal account behind the visual explanation." }, { "id": "transformers-kv-caching-explained", "reason": "Continue from attention computations to the KV cache used during generation." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/transformers-visual", "status": "curated" }, { "id": "introduction-information-retrieval", "slug": "introduction-information-retrieval", "source": { "title": "Introduction to Information Retrieval", "type": "book", "canonicalUrl": "https://nlp.stanford.edu/IR-book/information-retrieval-book.html", "authors": [ { "name": "Christopher D. Manning", "type": "Person" }, { "name": "Prabhakar Raghavan", "type": "Person" }, { "name": "Hinrich Schütze", "type": "Person" } ], "publisher": { "name": "Cambridge University Press", "url": "https://www.cambridge.org" }, "datePublished": null, "access": "public", "schemaType": "Book", "publicationYear": 2008, "isbn": "9780521865715", "pdfUrl": "https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf", "preview": { "id": "introduction-information-retrieval", "sourcePage": "https://nlp.stanford.edu/IR-book/information-retrieval-book.html", "sourceImage": "https://nlp.stanford.edu/IR-book/iir.jpg", "credit": "Cambridge University Press", "sourceWidth": 180, "sourceHeight": 265, "width": 180, "height": 265, "path": "/img/library/introduction-information-retrieval.webp", "thumbnail": { "path": "/img/library/introduction-information-retrieval-small.webp", "width": 180, "height": 265 }, "alt": "Book cover of Introduction to Information Retrieval." } }, "summary": "A textbook on indexing, ranking, evaluation, classification and web search, with a freely available online edition and teaching materials.", "curation": { "note": null, "listingNote": "The foundations of search, from inverted indexes to retrieval evaluation." }, "topics": [ "Engineering", "AI & machine learning" ], "tags": [ "Information retrieval", "Search", "Ranking" ], "related": [ { "id": "improved-text-scoring-with-bm25", "reason": "See BM25 ranking applied in an Elasticsearch presentation." }, { "id": "rag-is-more-than-just-embedding-search", "reason": "Connect classical retrieval foundations with the design of modern RAG systems." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/introduction-information-retrieval", "status": "curated" }, { "id": "data-intensive-applications", "slug": "data-intensive-applications", "source": { "title": "Designing Data-Intensive Applications", "type": "book", "canonicalUrl": "https://dataintensive.net/", "authors": [ { "name": "Martin Kleppmann", "type": "Person" } ], "publisher": { "name": "O’Reilly Media", "url": "https://www.oreilly.com" }, "datePublished": null, "access": "purchase", "schemaType": "Book", "publicationYear": 2017, "isbn": "9781491903063", "preview": { "id": "data-intensive-applications", "sourcePage": "https://dataintensive.net/", "sourceImage": "https://dataintensive.net/images/book-cover.png", "credit": "O’Reilly Media", "sourceWidth": 748, "sourceHeight": 982, "width": 748, "height": 982, "path": "/img/library/data-intensive-applications.webp", "thumbnail": { "path": "/img/library/data-intensive-applications-small.webp", "width": 480, "height": 630 }, "alt": "Book cover of Designing Data-Intensive Applications." }, "edition": "1" }, "summary": "The first edition examines the trade-offs behind reliable data systems, including storage, replication, transactions, stream processing and distributed consistency.", "curation": { "note": null, "listingNote": "Understand the trade-offs behind reliable, scalable data systems." }, "topics": [ "Engineering" ], "tags": [ "Architecture", "Distributed systems", "Databases" ], "related": [ { "id": "pgmq", "reason": "A concrete PostgreSQL-based queue for the book’s stream-processing and storage questions." }, { "id": "pgvector-pgvector", "reason": "Another example of extending a database to support a new workload." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/data-intensive-applications", "status": "curated" }, { "id": "llm-inference-performance", "slug": "llm-inference-performance", "source": { "title": "LLM Inference Performance Engineering: Best Practices", "type": "article", "canonicalUrl": "https://www.databricks.com/blog/llm-inference-performance-engineering-best-practices", "authors": [ { "name": "Megha Agarwal", "type": "Person" }, { "name": "Asfandyar Qureshi", "type": "Person" }, { "name": "Nikhil Sardana", "type": "Person" }, { "name": "Linden Li", "type": "Person" }, { "name": "Julian Quevedo", "type": "Person" }, { "name": "Daya Khudia", "type": "Person" } ], "publisher": { "name": "Databricks", "url": "https://www.databricks.com" }, "datePublished": "2023-10-12", "access": "public", "schemaType": "Article", "preview": { "id": "llm-inference-performance", "sourcePage": "https://www.databricks.com/blog/llm-inference-performance-engineering-best-practices", "sourceImage": "https://www.databricks.com/sites/default/files/2025-01/llm-inference-performance-engineering-best-practices.png", "credit": "Databricks", "sourceWidth": 3532, "sourceHeight": 1256, "width": 1200, "height": 427, "path": "/img/library/llm-inference-performance.webp", "thumbnail": { "path": "/img/library/llm-inference-performance-small.webp", "width": 480, "height": 171 }, "alt": "Original preview for LLM Inference Performance Engineering: Best Practices." } }, "summary": "An engineering guide to LLM inference latency, throughput and cost, covering GPU utilization, batching, memory and the difference between prefill and decoding.", "curation": { "note": null, "listingNote": "Measure and improve the latency, throughput and cost of serving LLMs." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Inference", "Performance", "Benchmarking" ], "related": [ { "id": "pagedattention", "reason": "PagedAttention is a specific technique for improving serving memory efficiency." }, { "id": "calculating-gpu-memory-for-serving-llms", "reason": "Estimate the GPU memory budget before tuning inference performance." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/llm-inference-performance", "status": "curated" }, { "id": "semiconductor-resilience", "slug": "semiconductor-resilience", "source": { "title": "Emerging Resilience in the Semiconductor Supply Chain", "type": "article", "canonicalUrl": "https://www.bcg.com/publications/2024/emerging-resilience-in-semiconductor-supply-chain", "authors": [ { "name": "Raj Varadarajan", "type": "Person" }, { "name": "Iacob Koch-Weser", "type": "Person" }, { "name": "Christopher Richard", "type": "Person" }, { "name": "Joseph Fitzgerald", "type": "Person" }, { "name": "Jaskaran Singh", "type": "Person" }, { "name": "Mary Thornton", "type": "Person" }, { "name": "Robert Casanova", "type": "Person" } ], "publisher": { "name": "Boston Consulting Group", "url": "https://www.bcg.com" }, "datePublished": "2024-05-08", "access": "public", "schemaType": "Article", "preview": { "id": "semiconductor-resilience", "sourcePage": "https://www.bcg.com/publications/2024/emerging-resilience-in-semiconductor-supply-chain", "sourceImage": "https://boston-consulting-group-brightspot.s3.amazonaws.com/46/e1/13a4ce354f04b5a00056f32c534a/emerging-resilience-in-the-semiconductor-supply-chain-rectangle.jpg", "credit": "Boston Consulting Group", "sourceWidth": 2880, "sourceHeight": 1617, "width": 1200, "height": 674, "path": "/img/library/semiconductor-resilience.webp", "thumbnail": { "path": "/img/library/semiconductor-resilience-small.webp", "width": 480, "height": 270 }, "alt": "Original preview for Emerging Resilience in the Semiconductor Supply Chain." } }, "summary": "BCG and the Semiconductor Industry Association examine geographic concentration, industrial policy and investment plans across the semiconductor supply chain.", "curation": { "note": null, "listingNote": "How industrial policy and new investment could change semiconductor dependencies." }, "topics": [ "Strategy & sovereignty", "Engineering" ], "tags": [ "Semiconductors", "Supply chains", "AI infrastructure" ], "related": [ { "id": "chip-war", "reason": "The industry history explains the dependencies behind today’s resilience debate." }, { "id": "european-chips-survey", "reason": "Compare the global supply-chain analysis with European demand and supply constraints." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/semiconductor-resilience", "status": "curated", "citedIn": [ { "title": "Large Language Models : Souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "chip-war", "slug": "chip-war", "source": { "title": "Chip War: The Fight for the World’s Most Critical Technology", "type": "book", "canonicalUrl": "https://www.simonandschuster.com/books/Chip-War/Chris-Miller/9781982172008", "authors": [ { "name": "Chris Miller", "type": "Person" } ], "publisher": { "name": "Scribner", "url": "https://www.simonandschuster.com" }, "datePublished": "2022-10-04", "access": "purchase", "schemaType": "Book", "isbn": "9781982172008", "preview": { "id": "chip-war", "sourcePage": "https://www.simonandschuster.com/books/Chip-War/Chris-Miller/9781982172008", "sourceImage": "https://d28hgpri8am2if.cloudfront.net/book_images/onix/cvr9781982172008/chip-war-9781982172008_hr.jpg", "credit": "Scribner", "sourceWidth": 1399, "sourceHeight": 2114, "width": 1059, "height": 1600, "path": "/img/library/chip-war.webp", "thumbnail": { "path": "/img/library/chip-war-small.webp", "width": 424, "height": 640 }, "alt": "Book cover of Chip War: The Fight for the World’s Most Critical Technology." } }, "summary": "A history of the semiconductor industry and the economic, military and geopolitical competition surrounding chip design and manufacturing.", "curation": { "note": null, "listingNote": "The industrial and geopolitical history behind today’s computing infrastructure." }, "topics": [ "Strategy & sovereignty", "Engineering" ], "tags": [ "Semiconductors", "Geopolitics", "Industrial strategy" ], "related": [ { "id": "reverse-engineering-the-semiconductor-industry", "reason": "A complementary explanation of the industry’s manufacturing models and dependencies." }, { "id": "european-chips-act", "reason": "See how semiconductor competition translates into European industrial policy." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/chip-war", "status": "curated", "citedIn": [ { "title": "Large 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480, "height": 252 } } }, "summary": "Specifications and architectural context for NVIDIA’s H100 accelerator.", "curation": { "note": null, "listingNote": "Specifications and architectural context for NVIDIA’s H100 accelerator." }, "topics": [ "Engineering" ], "tags": [ "Inference" ], "related": [ { "id": "nvidia-a100-tensor-core-gpu-datasheet", "reason": "Compare H100 specifications with the A100 accelerator’s datasheet." }, { "id": "nvidia-system-management-interface-nvidia-smi", "reason": "Inspect and manage GPU hardware through NVIDIA’s command-line interface." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/nvidia-h100-gpu", "status": "curated" }, { "id": "bert-pre-training-of-deep-bidirectional-transformers-for-language-understanding", "slug": "bert-pre-training-of-deep-bidirectional-transformers-for-language-understanding", "source": { "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/1810.04805v2", "authors": [ { "name": "Jacob Devlin", "type": "Person" }, { "name": "Ming-Wei Chang", "type": "Person" }, { "name": "Kenton Lee", "type": "Person" }, { "name": "Kristina Toutanova", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2018-10-11", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/1810.04805v2", "preview": { "path": "/img/library/ref-955cabf3213d.webp", "width": 1132, "height": 1600, "sourcePage": "https://arxiv.org/abs/1810.04805v2", "sourceImage": "https://arxiv.org/pdf/1810.04805v2", "sourceWidth": 1132, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.", "thumbnail": { "path": "/img/library/ref-955cabf3213d-small.webp", "width": 453, "height": 640 } } }, "summary": "The BERT paper introduces bidirectional pre-training for language understanding.", "curation": { "note": null, "listingNote": "The BERT paper introduces bidirectional pre-training for language understanding." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "attention-is-all-you-need", "reason": "The Transformer architecture underlies BERT’s bidirectional pre-training." }, { "id": "huggingface-sentence-transformers", "reason": "Explore practical embedding models for downstream language-understanding tasks." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/bert-pre-training-of-deep-bidirectional-transformers-for-language-understanding", "status": "curated", "citedIn": [ { "title": "Industrialiser la GenAI : Build vs Buy", "url": "https://fpaupier.fr/202501_productionize_genai" } ] }, { "id": "improving-your-llms-with-rlhf-on-amazon-sagemaker", "slug": "improving-your-llms-with-rlhf-on-amazon-sagemaker", "source": { "title": "Improving your LLMs with RLHF on Amazon SageMaker", "type": "article", "canonicalUrl": "https://aws.amazon.com/blogs/machine-learning/improving-your-llms-with-rlhf-on-amazon-sagemaker/", "authors": [], "publisher": { "name": "Amazon Web Services", "url": "https://aws.amazon.com" }, "datePublished": "2023-09-22", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-8d52dd716fab.webp", "width": 1200, "height": 532, "sourcePage": "https://aws.amazon.com/blogs/machine-learning/improving-your-llms-with-rlhf-on-amazon-sagemaker/", "sourceImage": "https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2023/09/22/ML-14874_image001-1260x559.jpg", "sourceWidth": 1260, "sourceHeight": 559, "credit": "Amazon Web Services", "thumbnail": { "path": "/img/library/ref-8d52dd716fab-small.webp", "width": 480, "height": 213 } } }, "summary": "A worked implementation of reinforcement learning from human feedback using SageMaker.", "curation": { "note": null, "listingNote": "A worked implementation of reinforcement learning from human feedback using SageMaker." }, "topics": [ "AI & machine learning" ], "tags": [ "Model adaptation" ], "related": [ { "id": "tune-palm-text-models-by-using-rlhf-tuning", "reason": "Compare two cloud implementations of reinforcement learning from human feedback." }, { "id": "direct-preference-optimization-your-language-model-is-secretly-a-reward-model", "reason": "DPO offers a different approach to learning from preference data." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/improving-your-llms-with-rlhf-on-amazon-sagemaker", "status": "curated" }, { "id": "tune-palm-text-models-by-using-rlhf-tuning", "slug": "tune-palm-text-models-by-using-rlhf-tuning", "source": { "title": "Tune PaLM text models by using RLHF tuning", "type": "documentation", "canonicalUrl": "https://cloud.google.com/vertex-ai/docs/generative-ai/models/tune-text-models-rlhf", "authors": [], 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} ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/llm-api-pricing-calculator", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "eliza", "slug": "eliza", "source": { "title": "ELIZA", "type": "documentation", "canonicalUrl": "https://en.wikipedia.org/wiki/ELIZA", "authors": [], "publisher": { "name": "Wikipedia", "url": "https://en.wikipedia.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-d5c55e3bb97c.webp", "width": 751, "height": 487, "sourcePage": "https://en.wikipedia.org/wiki/ELIZA", "sourceImage": "https://upload.wikimedia.org/wikipedia/commons/7/79/ELIZA_conversation.png?utm_source=en.wikipedia.org&utm_campaign=index&utm_content=thumbnail_unscaled", "sourceWidth": 751, "sourceHeight": 487, "credit": "Wikipedia", "thumbnail": { "path": "/img/library/ref-d5c55e3bb97c-small.webp", "width": 480, "height": 311 } } }, "summary": "An overview of ELIZA and its place in the history of conversational software.", "curation": { "note": null, "listingNote": "An overview of ELIZA and its place in the history of conversational software." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "large-language-models-101-history-evolution-and-future", "reason": "Place an early conversational program within the longer development of language models." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/eliza", "status": "curated" }, { "id": "long-short-term-memory", "slug": "long-short-term-memory", "source": { "title": "Long short-term memory", "type": "documentation", "canonicalUrl": "https://en.wikipedia.org/wiki/Long_short-term_memory", "authors": [], "publisher": { "name": "Wikipedia", "url": "https://en.wikipedia.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-db1074e8ad0b.webp", "width": 1200, "height": 850, "sourcePage": "https://en.wikipedia.org/wiki/Long_short-term_memory", "sourceImage": "https://thumb.wikimedia.org/wikipedia/commons/thumb/7/7d/NN_LSTM-Cell_v2.svg/1280px-NN_LSTM-Cell_v2.svg.png?utm_source=en.wikipedia.org&utm_campaign=index&utm_content=thumbnail", "sourceWidth": 1280, "sourceHeight": 907, "credit": "Wikipedia", "thumbnail": { "path": "/img/library/ref-db1074e8ad0b-small.webp", "width": 480, "height": 340 } } }, "summary": "Background on recurrent networks designed to preserve information across long sequences.", "curation": { "note": null, "listingNote": "Background on recurrent networks designed to preserve information across long sequences." }, "topics": [ "Engineering", "Product & design" ], "tags": [ "Inference", "Product engineering" ], "related": [ { "id": "speech-and-language-processing-rnns-and-lstms", "reason": "A textbook treatment of recurrent networks and long short-term memory models." }, { "id": "attention-is-all-you-need", "reason": "Contrast recurrence with the attention-based Transformer architecture." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/long-short-term-memory", "status": "curated" }, { "id": "aubio-aubio", "slug": "aubio-aubio", "source": { "title": "aubio/aubio", "type": "repository", "canonicalUrl": "https://github.com/aubio/aubio/", "authors": [], "publisher": { "name": "GitHub", "url": "https://github.com" }, "datePublished": null, "access": "public", "schemaType": "SoftwareSourceCode", "preview": { "path": "/img/library/ref-bf7e6ee2c324.webp", "width": 1200, "height": 600, "sourcePage": "https://github.com/aubio/aubio/", "sourceImage": "https://opengraph.githubassets.com/ef01c47bd6b90ccd36a6d382209ebfc6bb384ed8acf9c01beaedb78104688649/aubio/aubio", "sourceWidth": 1200, "sourceHeight": 600, "credit": "GitHub", "thumbnail": { "path": 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context to a model." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/zylon-ai-private-gpt", "status": "curated" }, { "id": "gradient-blog-rag-101-for-enterprise", "slug": "gradient-blog-rag-101-for-enterprise", "source": { "title": "Gradient Blog: RAG 101 for Enterprise", "type": "article", "canonicalUrl": "https://gradient.ai/blog/rag-101-for-enterprise", "authors": [], "publisher": { "name": "gradient.ai", "url": "https://gradient.ai" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://web.archive.org/web/20231203194442/https://gradient.ai/blog/rag-101-for-enterprise", "sourceImage": "https://framerusercontent.com/images/TqutMQRZV4GX5xiDpRfBU6KWQ.png", "credit": "Gradient", "kind": "source-image", "alt": "Original Gradient article illustration showing a person in conversation with a robot.", "sourceWidth": 1009, "sourceHeight": 615, "path": "/img/library/gradient-blog-rag-101-for-enterprise.webp", "width": 1009, "height": 615, "thumbnail": { "path": "/img/library/gradient-blog-rag-101-for-enterprise-small.webp", "width": 480, "height": 293 } } }, "summary": "An introduction to retrieval-augmented generation for enterprise applications.", "curation": { "note": null, "listingNote": "An introduction to retrieval-augmented generation for enterprise applications." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "rag-is-more-than-just-embedding-search", "reason": "Move beyond the introductory RAG pattern to the additional work retrieval requires." }, { "id": "the-rag-triad", "reason": "Evaluate retrieval context, answer grounding, and answer relevance separately." } ], "archive": { "snapshotUrl": "https://web.archive.org/web/20250521102819/https://www.gradient.ai/blog/rag-101-for-enterprise" }, "detailUrl": "https://fpaupier.fr/library/gradient-blog-rag-101-for-enterprise", "status": "curated" }, { "id": "bigscience-xp3all", "slug": "bigscience-xp3all", "source": { "title": "bigscience/xP3all", "type": "website", "canonicalUrl": "https://huggingface.co/datasets/bigscience/xP3all", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-b1f4b3679548.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/datasets/bigscience/xP3all", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/datasets/bigscience/xP3all.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-b1f4b3679548-small.webp", "width": 480, "height": 259 } } }, "summary": "A multilingual collection of prompted datasets for instruction tuning.", "curation": { "note": null, "listingNote": "A multilingual collection of prompted datasets for instruction tuning." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "huggingfaceh4-instruction-dataset", "reason": "Compare another instruction-tuning dataset with xP3’s multilingual collection." }, { "id": "aligning-language-models-to-follow-instructions", "reason": "Instruction-following research explains a use case for prompted training data." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/bigscience-xp3all", "status": "curated" }, { "id": "what-are-agents", "slug": "what-are-agents", "source": { "title": "What are agents? 🤔", "type": "documentation", "canonicalUrl": "https://huggingface.co/docs/smolagents/en/conceptual_guides/intro_agents", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-d7759433f996.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/docs/smolagents/en/conceptual_guides/intro_agents", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/docs/smolagents/conceptual_guides/intro_agents.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-d7759433f996-small.webp", "width": 480, "height": 259 } } }, "summary": "An introduction to agents, their components, and tool use.", "curation": { "note": null, "listingNote": "An introduction to agents, their components, and tool use." }, "topics": [ "AI & machine learning" ], "tags": [ "Agents" ], "related": [ { "id": "model-context-protocol", "reason": "The Model Context Protocol addresses how applications connect to tools and data." }, { "id": "deepsearch-deepresearch", "reason": "Deep research provides a concrete example of an iterative agent workflow." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/what-are-agents", "status": "curated", "citedIn": [ { "title": "Industrialiser la GenAI : Build vs Buy", "url": "https://fpaupier.fr/202501_productionize_genai" } ] }, { "id": "text-generation", "slug": "text-generation", "source": { "title": "Text generation", "type": "documentation", "canonicalUrl": "https://huggingface.co/docs/transformers/en/llm_tutorial", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-49111f7b684e.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/docs/transformers/en/llm_tutorial", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/docs/transformers/llm_tutorial.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-49111f7b684e-small.webp", "width": 480, "height": 259 } } }, "summary": "Guidance for generating text with the Transformers library.", "curation": { "note": null, "listingNote": "Guidance for generating text with the Transformers library." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "text-generation-inference", "reason": "Move from library-level generation to a dedicated text-generation inference server." }, { "id": "transformers-kv-caching-explained", "reason": "KV caching explains an important optimization in autoregressive generation." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/text-generation", "status": "curated" }, { "id": "mistralai-mistral-small-24b-instruct-2501", "slug": "mistralai-mistral-small-24b-instruct-2501", "source": { "title": "mistralai/Mistral-Small-24B-Instruct-2501", "type": "website", "canonicalUrl": "https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-722470aa49ce.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/models/mistralai/Mistral-Small-24B-Instruct-2501.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-722470aa49ce-small.webp", "width": 480, "height": 259 } } }, "summary": "The model card for Mistral Small 24B Instruct 2501.", "curation": { "note": null, "listingNote": "The model card for Mistral Small 24B Instruct 2501." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "mistralai-mistral-small-3-1-24b-instruct-2503", "reason": "Compare the January Mistral Small model with the later 3.1 instruction-tuned release." }, { "id": "models-overview", "reason": "The vendor’s model overview places this release within the wider family." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/mistralai-mistral-small-24b-instruct-2501", "status": "curated" }, { "id": "ethical-charter-a-hugging-face-space-by-bigscience", "slug": "ethical-charter-a-hugging-face-space-by-bigscience", "source": { "title": "Ethical Charter - a Hugging Face Space by bigscience", "type": "website", "canonicalUrl": "https://huggingface.co/spaces/bigscience/ethical-charter", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-e28b75e446ad.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/spaces/bigscience/ethical-charter", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/spaces/bigscience/ethical-charter.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-e28b75e446ad-small.webp", "width": 480, "height": 259 } } }, "summary": "The ethical charter created for the BigScience research collaboration.", "curation": { "note": null, "listingNote": "The ethical charter created for the BigScience research collaboration." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "opening-up-chatgpt", "reason": "Compare research-collaboration principles with practical criteria for model openness." }, { "id": "model-cards", "reason": "Model cards make intended uses and limitations explicit for model users." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/ethical-charter-a-hugging-face-space-by-bigscience", "status": "curated" }, { "id": "ecologits-calculator", "slug": "ecologits-calculator", "source": { "title": "EcoLogits Calculator", "type": "website", "canonicalUrl": "https://calculator.ecologits.ai/", "authors": [], "publisher": { "name": "calculator.ecologits.ai", "url": "https://calculator.ecologits.ai" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-07ba099a445a.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/spaces/genai-impact/ecologits-calculator", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/spaces/genai-impact/ecologits-calculator.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "huggingface.co", "thumbnail": { "path": "/img/library/ref-07ba099a445a-small.webp", "width": 480, "height": 259 } } }, "summary": "A calculator for estimating the environmental impacts of generative-AI usage.", "curation": { "note": null, "listingNote": "A calculator for estimating the environmental impacts of generative-AI usage." }, "topics": [ "AI & machine learning" ], "tags": [ "Sustainability" ], "related": [ { "id": "the-environmental-impacts-of-ai-policy-primer", "reason": "The policy primer explains the resource and emissions categories behind impact estimates." }, { "id": "a-survey-of-sustainability-in-large-language-models-applications-economics-and-challe", "reason": "A research survey provides broader context for measuring language-model sustainability." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/ecologits-calculator", "status": "curated", "citedIn": [ { "title": "LLM : souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "how-people-create-and-destroy-value-with-generative-ai", "slug": "how-people-create-and-destroy-value-with-generative-ai", "source": { "title": "How People Create and Destroy Value with Generative AI", "type": "report", "canonicalUrl": "https://mkt-bcg-com-public-pdfs.s3.amazonaws.com/prod/how-people-create-and-destroy-value-with-gen-ai.pdf", "authors": [], "publisher": { "name": "Boston Consulting Group", "url": "https://www.bcg.com/" }, "datePublished": null, "access": "public", "schemaType": "Report", "preview": { "sourcePage": "https://mkt-bcg-com-public-pdfs.s3.amazonaws.com/prod/how-people-create-and-destroy-value-with-gen-ai.pdf", "sourceImage": "https://mkt-bcg-com-public-pdfs.s3.amazonaws.com/prod/how-people-create-and-destroy-value-with-gen-ai.pdf", "credit": "Boston Consulting Group", "kind": "document-page", "alt": "First page of How People Create and Destroy Value with Generative AI.", "sourceWidth": 1273, "sourceHeight": 1800, "path": "/img/library/how-people-create-and-destroy-value-with-generative-ai.webp", "width": 1132, "height": 1600, "thumbnail": { "path": "/img/library/how-people-create-and-destroy-value-with-generative-ai-small.webp", "width": 453, "height": 640 }, "pageNumber": 1 } }, "summary": "Research into how generative AI changes knowledge-work performance.", "curation": { "note": null, "listingNote": "Research into how generative AI changes knowledge-work performance." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "switching-perspectives-on-generative-artificial-intelligence-a-design-view-for-humans", "reason": "Compare measured changes in knowledge work with a design view of human-AI collaboration." }, { "id": "gen-ai-at-work-the-story-of-carrefour-marketing-studio-horizons-by-carrefour", "reason": "A concrete marketing deployment shows generative AI entering a professional workflow." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/how-people-create-and-destroy-value-with-generative-ai", "status": "curated" }, { "id": "aligning-language-models-to-follow-instructions", "slug": "aligning-language-models-to-follow-instructions", "source": { "title": "Aligning language models to follow instructions", "type": "article", "canonicalUrl": "https://openai.com/index/instruction-following/", "authors": [], "publisher": { "name": "OpenAI", "url": "https://openai.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-ba1b76682ebb.webp", "width": 1200, "height": 675, "sourcePage": "https://openai.com/index/instruction-following/", "sourceImage": "https://images.ctfassets.net/kftzwdyauwt9/5b1e6906-9f64-42b7-b5d87c29543b/25be46c3552b7af3af0a1c5a70381626/image-13.webp?w=1600&h=900&fit=fill", "sourceWidth": 1600, "sourceHeight": 900, "credit": "OpenAI", "thumbnail": { "path": "/img/library/ref-ba1b76682ebb-small.webp", "width": 480, "height": 270 } } }, "summary": "OpenAI’s explanation of instruction-following models trained using human feedback.", "curation": { "note": null, "listingNote": "OpenAI’s explanation of instruction-following models trained using human feedback." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "improving-your-llms-with-rlhf-on-amazon-sagemaker", "reason": "See a worked RLHF implementation of learning from human feedback." }, { "id": "direct-preference-optimization-your-language-model-is-secretly-a-reward-model", "reason": "Contrast this alignment approach with direct preference optimization." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/aligning-language-models-to-follow-instructions", "status": "curated" }, { "id": "stanford-corenlp", "slug": "stanford-corenlp", "source": { "title": "Stanford CoreNLP", "type": "documentation", "canonicalUrl": "https://stanfordnlp.github.io/CoreNLP/index.html", "authors": [], "publisher": { "name": "CoreNLP", "url": "https://stanfordnlp.github.io" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-ba49462df79f.webp", "width": 1080, "height": 360, "sourcePage": "https://stanfordnlp.github.io/CoreNLP/index.html", "sourceImage": "https://stanfordnlp.github.io/CoreNLP/assets/images/corenlp-logo.png", "sourceWidth": 1080, "sourceHeight": 360, "credit": "CoreNLP", "thumbnail": { "path": "/img/library/ref-ba49462df79f-small.webp", "width": 480, "height": 160 } } }, "summary": "Stanford’s toolkit for annotating and analyzing text with natural-language processing methods.", "curation": { "note": null, "listingNote": "Stanford’s toolkit for annotating and analyzing text with natural-language processing methods." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "large-language-models-101-history-evolution-and-future", "reason": "Place a traditional NLP toolkit within the history of language-processing systems." }, { "id": "bert-pre-training-of-deep-bidirectional-transformers-for-language-understanding", "reason": "BERT represents a later pre-trained approach to language understanding." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/stanford-corenlp", "status": "curated" }, { "id": "amazon-and-anthropic-announce-strategic-collaboration-to-advance-generative-ai", "slug": "amazon-and-anthropic-announce-strategic-collaboration-to-advance-generative-ai", "source": { "title": "Amazon and Anthropic announce strategic collaboration to advance generative AI", "type": "article", "canonicalUrl": "https://www.aboutamazon.com/news/company-news/amazon-aws-anthropic-ai", "authors": [ { "name": "Amazon Staff", "type": "Organization" } ], "publisher": { "name": "Amazon News", "url": "https://www.aboutamazon.com" }, "datePublished": "2023-09-25", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-267b74b0912d.webp", "width": 1200, "height": 600, "sourcePage": "https://www.aboutamazon.com/news/company-news/amazon-aws-anthropic-ai", "sourceImage": "https://assets.aboutamazon.com/dims4/default/3ec5141/2147483647/strip/true/crop/2624x1312+0+656/resize/1200x600!/quality/90/?url=https%3A%2F%2Fassets.aboutamazon.com%2F08%2F32%2F14b251fb4b6cac3397807a7f7720%2Famazon-anthropic.png", "sourceWidth": 1200, "sourceHeight": 600, "credit": "Amazon News", "thumbnail": { "path": "/img/library/ref-267b74b0912d-small.webp", "width": 480, "height": 240 } } }, "summary": "The announcement of Amazon and Anthropic’s AI collaboration.", "curation": { "note": null, "listingNote": "The announcement of Amazon and Anthropic’s AI collaboration." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "chatgpt-and-generative-ai-are-booming-but-the-costs-can-be-extraordinary", "reason": "Infrastructure economics helps explain the context of large AI partnerships." }, { "id": "la-start-up-francaise-mistral-ai-leve-105-millions-d-euros-pour-developper-des-modele", "reason": "Compare a strategic partnership with an early-stage model company’s fundraising." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/amazon-and-anthropic-announce-strategic-collaboration-to-advance-generative-ai", "status": "curated" }, { "id": "la-bnf-en-chiffres", "slug": "la-bnf-en-chiffres", "source": { "title": "La BnF en chiffres", "type": "article", "canonicalUrl": "https://www.bnf.fr/fr/la-bnf-en-chiffres", "authors": [], "publisher": { "name": "BnF - Site institutionnel", "url": "https://www.bnf.fr" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://www.bnf.fr/fr/la-bnf-en-chiffres", "credit": "BnF - Site institutionnel", "kind": "screenshot", "alt": "Screenshot of La BnF en chiffres", "sourceWidth": 1200, "sourceHeight": 780, "path": "/img/library/la-bnf-en-chiffres.webp", "width": 1200, "height": 780, "thumbnail": { "path": "/img/library/la-bnf-en-chiffres-small.webp", "width": 480, "height": 312 }, "capturedAt": "2026-10-13" } }, "summary": "Institutional statistics from the Bibliothèque nationale de France.", "curation": { "note": null, "listingNote": "Institutional statistics from the Bibliothèque nationale de France." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "the-battle-over-books3-could-change-ai-forever", "reason": "The scale of book collections provides context for debates over book-based training datasets." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/la-bnf-en-chiffres", "status": "curated" }, { "id": "ia-cette-celebre-comedienne-americaine-poursuit-meta-et-openai-pour-violation-de-droi", "slug": "ia-cette-celebre-comedienne-americaine-poursuit-meta-et-openai-pour-violation-de-droi", "source": { "title": "IA : cette célèbre comédienne américaine poursuit Meta et OpenAI pour violation de droits d'auteur", "type": "article", "canonicalUrl": "https://www.clubic.com/meta/actualite-477253-ia-cette-celebre-comedienne-americaine-poursuit-meta-et-openai-pour-violation-de-droits-d-auteur.html", "authors": [], "publisher": { "name": "clubic.com", "url": "https://www.clubic.com" }, "datePublished": "2023-07-10", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-52c54afc8d00.webp", "width": 1200, "height": 800, "sourcePage": "https://www.clubic.com/meta/actualite-477253-ia-cette-celebre-comedienne-americaine-poursuit-meta-et-openai-pour-violation-de-droits-d-auteur.html", "sourceImage": "https://pic.clubic.com/v1/images/2087049/raw", "sourceWidth": 2000, "sourceHeight": 1334, "credit": "clubic.com", "thumbnail": { "path": "/img/library/ref-52c54afc8d00-small.webp", "width": 480, "height": 320 } } }, "summary": "Reporting on a copyright dispute involving generative-AI training data.", "curation": { "note": null, "listingNote": "Reporting on a copyright dispute involving generative-AI training data." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "the-battle-over-books3-could-change-ai-forever", "reason": "A second account examines the copyright dispute around books used for AI training." }, { "id": "ethical-charter-a-hugging-face-space-by-bigscience", "reason": "The BigScience charter provides a contrasting discussion of research responsibilities." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/ia-cette-celebre-comedienne-americaine-poursuit-meta-et-openai-pour-violation-de-droi", "status": "curated" }, { "id": "d-day-la-famille-du-veteran-leon-gautier-dit-stop-au-business-memoriel", "slug": "d-day-la-famille-du-veteran-leon-gautier-dit-stop-au-business-memoriel", "source": { "title": "D-Day : la famille du vétéran Léon Gautier dit stop au business mémoriel", "type": "article", "canonicalUrl": "https://www.france24.com/fr/france/20240229-d-day-la-famille-du-v%C3%A9t%C3%A9ran-l%C3%A9on-gautier-dit-stop-au-business-m%C3%A9moriel", "authors": [], "publisher": { "name": "France 24", "url": "https://www.france24.com" }, "datePublished": "2024-02-29", "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://www.france24.com/fr/france/20240229-d-day-la-famille-du-v%C3%A9t%C3%A9ran-l%C3%A9on-gautier-dit-stop-au-business-m%C3%A9moriel", "sourceImage": "https://s.france24.com/media/display/636e2574-d6e6-11ee-b70e-005056a97e36/w:1280/p:16x9/000_1HB03T.jpg", "credit": "AFP / Damien Meyer, via France 24", "kind": "source-image", "alt": "Léon Gautier at a commemoration for the Kieffer commandos in 2019.", "sourceWidth": 1280, "sourceHeight": 720, "path": "/img/library/d-day-la-famille-du-veteran-leon-gautier-dit-stop-au-business-memoriel.webp", "width": 1200, "height": 675, "thumbnail": { "path": "/img/library/d-day-la-famille-du-veteran-leon-gautier-dit-stop-au-business-memoriel-small.webp", "width": 480, "height": 270 } } }, "summary": "Reporting on objections to commercial uses of the memory of D-Day veteran Léon Gautier.", "curation": { "note": null, "listingNote": "Reporting on objections to commercial uses of the memory of D-Day veteran Léon Gautier." }, "topics": [ "Decision-making" ], "tags": [ "Media ethics", "Consent" ], "related": [ { "id": "ia-cette-celebre-comedienne-americaine-poursuit-meta-et-openai-pour-violation-de-droi", "reason": "Compare another dispute over permission and commercial reuse, here involving creative works used to train AI." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/d-day-la-famille-du-veteran-leon-gautier-dit-stop-au-business-memoriel", "status": "curated" }, { "id": "large-language-models-101-history-evolution-and-future", "slug": "large-language-models-101-history-evolution-and-future", "source": { "title": "Large Language Models 101: History, Evolution and Future", "type": "article", "canonicalUrl": "https://www.scribbledata.io/blog/large-language-models-history-evolutions-and-future/", "authors": [], "publisher": { "name": "Scribble Data", "url": "https://www.scribbledata.io" }, "datePublished": "2023-05-11", "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://www.scribbledata.io/blog/large-language-models-history-evolutions-and-future/", "credit": "Scribble Data", "kind": "source-image", "alt": "Timeline of large language models from ELIZA and recurrent networks to GPT-3", "sourceWidth": 1024, "sourceHeight": 576, "path": "/img/library/large-language-models-101-history-evolution-and-future.webp", "width": 1024, "height": 576, "thumbnail": { "path": "/img/library/large-language-models-101-history-evolution-and-future-small.webp", "width": 480, "height": 270 }, "sourceImage": "https://www.scribbledata.io/wp-content/uploads/2023/05/LLL_Evolution-02-1024x576.jpg" } }, "summary": "An introduction to the development and historical context of large language models.", "curation": { "note": null, "listingNote": "An introduction to the development and historical context of large language models." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "eliza", "reason": "ELIZA provides an early point of reference in conversational-software history." }, { "id": "1hr-talk-intro-to-large-language-models", "reason": "A technical lecture develops the language-model concepts introduced here." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/large-language-models-101-history-evolution-and-future", "status": "curated" }, { "id": "europarl-parallel-corpus", "slug": "europarl-parallel-corpus", "source": { "title": "Europarl Parallel Corpus", "type": "website", "canonicalUrl": "https://www.statmt.org/europarl/", "authors": [], "publisher": { "name": "statmt.org", "url": "https://www.statmt.org" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "sourcePage": "https://www.statmt.org/europarl/", "credit": "statmt.org", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "Europarl corpus page with language-pair downloads and European Parliament dataset releases.", "sourceWidth": 1200, "sourceHeight": 850, "path": "/img/library/europarl-parallel-corpus.webp", "width": 1200, "height": 850, "thumbnail": { "path": "/img/library/europarl-parallel-corpus-small.webp", "width": 480, "height": 340 } } }, "summary": "A parallel corpus built from European Parliament proceedings.", "curation": { "note": null, "listingNote": "A parallel corpus built from European Parliament proceedings." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "bigscience-xp3all", "reason": "Compare a parallel-language corpus with multilingual prompted training datasets." }, { "id": "efficient-estimation-of-word-representations-in-vector-space", "reason": "Word-representation research offers context for how language data becomes model input." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/europarl-parallel-corpus", "status": "curated" }, { "id": "the-battle-over-books3-could-change-ai-forever", "slug": "the-battle-over-books3-could-change-ai-forever", "source": { "title": "The Battle Over Books3 Could Change AI Forever", "type": "article", "canonicalUrl": "https://www.wired.com/story/battle-over-books3/", "authors": [ { "name": "Kate Knibbs", "type": "Person" } ], "publisher": { "name": "WIRED", "url": "https://www.wired.com" }, "datePublished": "2023-09-04", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-7bfb7f0e3edb.webp", "width": 1200, "height": 628, "sourcePage": "https://www.wired.com/story/battle-over-books3/", "sourceImage": "https://media.wired.com/photos/64f1255a6d9ef6802b127147/191:100/w_1280,c_limit/Battle-Over-Book3-Culture-159132659.jpg", "sourceWidth": 1280, "sourceHeight": 670, "credit": "WIRED", "thumbnail": { "path": "/img/library/ref-7bfb7f0e3edb-small.webp", "width": 480, "height": 251 } } }, "summary": "Reporting on the Books3 dataset and disputes about books used for AI training.", "curation": { "note": null, "listingNote": "Reporting on the Books3 dataset and disputes about books used for AI training." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "ia-cette-celebre-comedienne-americaine-poursuit-meta-et-openai-pour-violation-de-droi", "reason": "Another report follows copyright challenges to generative-AI training data." }, { "id": "opening-up-chatgpt", "reason": "Training-data transparency is one dimension in assessing model openness." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/the-battle-over-books3-could-change-ai-forever", "status": "curated" }, { "id": "1hr-talk-intro-to-large-language-models", "slug": "1hr-talk-intro-to-large-language-models", "source": { "title": "[1hr Talk] Intro to Large Language Models", "type": "video", "canonicalUrl": "https://www.youtube.com/watch?v=zjkBMFhNj_g", "authors": [], "publisher": { "name": "Andrej Karpathy", "url": "https://www.youtube.com" }, "datePublished": null, "access": "public", "schemaType": "VideoObject", "preview": { "id": "1hr-talk-intro-to-large-language-models", "sourcePage": "https://www.youtube.com/watch?v=zjkBMFhNj_g", "sourceImage": "https://i.ytimg.com/vi/zjkBMFhNj_g/maxresdefault.jpg", "credit": "Andrej Karpathy / YouTube", "sourceWidth": 1280, "sourceHeight": 720, "width": 1200, "height": 675, "path": "/img/library/1hr-talk-intro-to-large-language-models.webp", "thumbnail": { "path": "/img/library/1hr-talk-intro-to-large-language-models-small.webp", "width": 480, "height": 270 }, "alt": "Video thumbnail for [1hr Talk] Intro to Large Language Models.", "kind": "video-thumbnail" } }, "summary": "Andrej Karpathy’s introductory lecture on large language models.", "curation": { "note": null, "listingNote": "Andrej Karpathy’s introductory lecture on large language models." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "transformers-visual", "reason": "A visual explanation develops the Transformer mechanism introduced in this lecture." }, { "id": "attention-is-all-you-need", "reason": "The original architecture paper supplies the formal foundation." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/1hr-talk-intro-to-large-language-models", "status": "curated" }, { "id": "knowledge-graph-deep-learning-a-case-study-in-question-answering-in-aviation-safety-d", "slug": "knowledge-graph-deep-learning-a-case-study-in-question-answering-in-aviation-safety-d", "source": { "title": "Knowledge Graph - Deep Learning: A Case Study in Question Answering in Aviation Safety Domain", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2205.15952v2", "authors": [ { "name": "Ankush Agarwal", "type": "Person" }, { "name": "Raj Gite", "type": "Person" }, { "name": "Shreya Laddha", "type": "Person" }, { "name": "Pushpak Bhattacharyya", "type": "Person" }, { "name": "Satyanarayan Kar", "type": "Person" }, { "name": "Asif Ekbal", "type": "Person" }, { "name": "Prabhjit Thind", "type": "Person" }, { "name": "Rajesh Zele", "type": "Person" }, { "name": "Ravi Shankar", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2022-05-31", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2205.15952v2", "preview": { "path": "/img/library/ref-4e1eee128619.webp", "width": 1132, "height": 1600, "sourcePage": "https://arxiv.org/abs/2205.15952v2", "sourceImage": "https://arxiv.org/pdf/2205.15952v2", "sourceWidth": 1132, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Knowledge Graph - Deep Learning: A Case Study in Question Answering in Aviation Safety Domain.", "thumbnail": { "path": "/img/library/ref-4e1eee128619-small.webp", "width": 453, "height": 640 } } }, "summary": "A case study combining knowledge graphs and deep learning for aviation-safety questions.", "curation": { "note": null, "listingNote": "A case study combining knowledge graphs and deep learning for aviation-safety questions." }, "topics": [ "Engineering" ], "tags": [ "Knowledge graphs" ], "related": [ { "id": "rag-vs-graphrag", "reason": "Compare the aviation case study with a systematic RAG and GraphRAG evaluation." }, { "id": "injecting-knowledge-graphs-into-large-language-models", "reason": "Examine other approaches to bringing structured knowledge into language models." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/knowledge-graph-deep-learning-a-case-study-in-question-answering-in-aviation-safety-d", "status": "curated" }, { "id": "the-70-problem-hard-truths-about-ai-assisted-coding", "slug": "the-70-problem-hard-truths-about-ai-assisted-coding", "source": { "title": "The 70% problem: Hard truths about AI-assisted coding", "type": "article", "canonicalUrl": "https://addyo.substack.com/p/the-70-problem-hard-truths-about", "authors": [ { "name": "Addy Osmani", "type": "Person" } ], "publisher": { "name": "addyo.substack.com", "url": "https://addyo.substack.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-6ad4a4a38e5a.webp", "width": 1200, "height": 675, "sourcePage": "https://addyo.substack.com/p/the-70-problem-hard-truths-about", "sourceImage": "https://substackcdn.com/image/fetch/$s_!A6NX!,w_1200,h_675,c_fill,f_jpg,q_auto:good,fl_progressive:steep,g_auto/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F0e49ab22-0fac-4959-afa6-b6e226056db4_6072x6072.jpeg", "sourceWidth": 1200, "sourceHeight": 675, "credit": "addyo.substack.com", "thumbnail": { "path": "/img/library/ref-6ad4a4a38e5a-small.webp", "width": 480, "height": 270 } } }, "summary": "The remaining engineering work behind apparently near-complete AI-generated code.", "curation": { "note": null, "listingNote": "The remaining engineering work behind apparently near-complete AI-generated code." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "amplifying-coding-agent-intelligence", "reason": "A study of coding-agent tool choices examines another part of assisted development." }, { "id": "imitation-intelligence-my-keynote-for-pycon-us-2024", "reason": "A practitioner keynote offers wider context on using language models in software work." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/the-70-problem-hard-truths-about-ai-assisted-coding", "status": "curated" }, { "id": "ai-essentials-for-tech-executives", "slug": "ai-essentials-for-tech-executives", "source": { "title": "AI Essentials For Tech Executives", "type": "website", "canonicalUrl": "https://ai-execs.com/", "authors": [], "publisher": { "name": "AI Essentials For Tech Executives", "url": "https://ai-execs.com" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-bd77cf69044e.webp", "width": 1200, "height": 600, "sourcePage": "https://ai-execs.com/", "sourceImage": "https://ai-execs.com/cover.png", "sourceWidth": 1400, "sourceHeight": 700, "credit": "AI Essentials For Tech Executives", "thumbnail": { "path": "/img/library/ref-bd77cf69044e-small.webp", "width": 480, "height": 240 } } }, "summary": "Teaching material on AI systems and decisions for technology leaders.", "curation": { "note": null, "listingNote": "Teaching material on AI systems and decisions for technology leaders." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "how-people-create-and-destroy-value-with-generative-ai", "reason": "Measured effects on knowledge work inform decisions about AI adoption." }, { "id": "finding-the-right-llm-for-your-business-a-practical-cost-benefit-analysis", "reason": "A cost-benefit framework supports the model-selection decisions faced by technology leaders." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/ai-essentials-for-tech-executives", "status": "curated", "citedIn": [ { "title": "Industrialiser la GenAI : Build vs Buy", "url": "https://fpaupier.fr/202501_productionize_genai" } ] }, { "id": "introducing-meta-llama-3-the-most-capable-openly-available-llm-to-date", "slug": "introducing-meta-llama-3-the-most-capable-openly-available-llm-to-date", "source": { "title": "Introducing Meta Llama 3: The most capable openly available LLM to date", "type": "article", "canonicalUrl": "https://ai.meta.com/blog/meta-llama-3/", "authors": [], "publisher": { "name": "Meta AI", "url": "https://ai.meta.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-b0fb37ecc740.webp", "width": 1200, "height": 675, "sourcePage": "https://ai.meta.com/blog/meta-llama-3/", "sourceImage": "https://scontent-cdg4-2.xx.fbcdn.net/v/t39.2365-6/438904118_1185694632845028_8008633574873659221_n.jpg?_nc_cat=101&ccb=1-7&_nc_sid=e280be&_nc_ohc=3xL-wgiq2FcQ7kNvwHgJbl5&_nc_oc=Adro5O_ZOnCbBgVaI7ZBXtrvW5UYb88mF_aSQt8nApM7BITeoWuezr-WAIUgBJk125W8gkJRP4BZlUpZQCB2mOVP&_nc_zt=14&_nc_ht=scontent-cdg4-2.xx&_nc_gid=RXMr4TpNpI1ArQUoVajMEw&_nc_ss=7b20f&oh=00_AQNDtV7XxwX9UMEKhGgUWxcCSxMe0xkWH6Y0GPMHzd2RqA&oe=6AE87000", "sourceWidth": 3840, "sourceHeight": 2160, "credit": "Meta AI", "thumbnail": { "path": "/img/library/ref-b0fb37ecc740-small.webp", "width": 480, "height": 270 } } }, "summary": "Meta’s introduction to its Llama 3 model family.", "curation": { "note": null, "listingNote": "Meta’s introduction to its Llama 3 model family." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "meta-llama-llama-3-2-1b", "reason": "A later Llama model card provides a concrete continuation of the model family." }, { "id": "meta-llama-llama-2-7b-chat-hf", "reason": "Compare the release with an earlier Llama chat model." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/introducing-meta-llama-3-the-most-capable-openly-available-llm-to-date", "status": "curated" }, { "id": "mlops", "slug": "mlops", "source": { "title": "MLOps", "type": "article", "canonicalUrl": "https://airflow.apache.org/use-cases/mlops/", "authors": [], "publisher": { "name": "Apache Airflow", "url": "https://airflow.apache.org" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-c66ba7e55490.webp", "width": 1200, "height": 630, "sourcePage": "https://airflow.apache.org/use-cases/mlops/", "sourceImage": "https://airflow.apache.org/images/feature-image.png", "sourceWidth": 1200, "sourceHeight": 630, "credit": "Apache Airflow", "thumbnail": { "path": "/img/library/ref-c66ba7e55490-small.webp", "width": 480, "height": 252 } } }, "summary": "Apache Airflow examples for coordinating machine-learning workflows.", "curation": { "note": null, "listingNote": "Apache Airflow examples for coordinating machine-learning workflows." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "hidden-technical-debt", "reason": "Workflow orchestration is one part of managing machine-learning system dependencies." }, { "id": "15-le-ml-ops-pour-accelerer-linnovation-de-la-r-d-a-la-production-by-post-mortem", "reason": "A discussion of the organizational path from ML research to production." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/mlops", "status": "curated", "citedIn": [ { "title": "Industrialiser la GenAI : Build vs Buy", "url": "https://fpaupier.fr/202501_productionize_genai" } ] }, { "id": "qonto-how-qonto-achieved-70-faster-localization-and-streamlined-operations-with-ai-ag", "slug": "qonto-how-qonto-achieved-70-faster-localization-and-streamlined-operations-with-ai-ag", "source": { "title": "Qonto: How Qonto Achieved 70% Faster Localization and Streamlined Operations with AI Agents", "type": "article", "canonicalUrl": "https://dust.tt/customers/qonto-dust-ai-partnership", "authors": [], "publisher": { "name": "Dust", "url": "https://dust.tt" }, "datePublished": "2024-09-19", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-b85a7f6f54a2.webp", "width": 1200, "height": 672, "sourcePage": "https://dust.tt/customers/qonto-dust-ai-partnership", "sourceImage": "https://images.ctfassets.net/751ge4ljeqso/6MRFyaocG2EDXLGqSjmp59/986f7ecdbc8612ac87c33477b1f80170/image.png", "sourceWidth": 2000, "sourceHeight": 1120, "credit": "Dust", "thumbnail": { "path": "/img/library/ref-b85a7f6f54a2-small.webp", "width": 480, "height": 269 } } }, "summary": "Qonto’s account of using AI agents in localization and operational workflows.", "curation": { "note": null, "listingNote": "Qonto’s account of using AI agents in localization and operational workflows." }, "topics": [ "AI & machine learning" ], "tags": [ "Agents" ], "related": [ { "id": "what-are-agents", "reason": "Agent concepts explain the tools and workflows used in this deployment." }, { "id": "gen-ai-at-work-the-story-of-carrefour-marketing-studio-horizons-by-carrefour", "reason": "Compare another company’s use of generative AI in operational work." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/qonto-how-qonto-achieved-70-faster-localization-and-streamlined-operations-with-ai-ag", "status": "curated", "citedIn": [ { "title": "Industrialiser la GenAI : Build vs Buy", "url": "https://fpaupier.fr/202501_productionize_genai" } ] }, { "id": "vllm-v1-a-major-upgrade-to-vllm-s-core-architecture", "slug": "vllm-v1-a-major-upgrade-to-vllm-s-core-architecture", "source": { "title": "vLLM V1: A Major Upgrade to vLLM's Core Architecture", "type": "article", "canonicalUrl": "https://vllm.ai/blog/2025-01-27-v1-alpha-release", "authors": [ { "name": "vLLM Team", "type": "Organization" } ], "publisher": { "name": "vllm.ai", "url": "https://vllm.ai" }, "datePublished": "2025-01-27", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-150223aeef92.webp", "width": 1200, "height": 630, "sourcePage": "https://vllm.ai/blog/2025-01-27-v1-alpha-release", "sourceImage": "https://vllm.ai/og?title=vLLM%20V1%3A%20A%20Major%20Upgrade%20to%20vLLM%27s%20Core%20Architecture&authors=vLLM%20Team&date=2025-01-27&path=/blog", "sourceWidth": 1200, "sourceHeight": 630, "credit": "blog.vllm.ai", "thumbnail": { "path": "/img/library/ref-150223aeef92-small.webp", "width": 480, "height": 252 } } }, "summary": "The architectural changes introduced with vLLM V1.", "curation": { "note": null, "listingNote": "The architectural changes introduced with vLLM V1." }, "topics": [ "Engineering" ], "tags": [ "Inference" ], "related": [ { "id": "vllm-easy-fast-and-cheap-llm-serving-with-pagedattention", "reason": "Compare the architecture update with vLLM’s original serving announcement." }, { "id": "vllm-project-vllm", "reason": "The implementation repository is the next step for exploring the serving engine." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/vllm-v1-a-major-upgrade-to-vllm-s-core-architecture", "status": "curated" }, { "id": "15-le-ml-ops-pour-accelerer-linnovation-de-la-r-d-a-la-production-by-post-mortem", "slug": "15-le-ml-ops-pour-accelerer-linnovation-de-la-r-d-a-la-production-by-post-mortem", "source": { "title": "#15 Le ML Ops pour accélérer l’innovation, de la R&D à la production by Post Mortem", "type": "podcast", "canonicalUrl": "https://creators.spotify.com/pod/profile/podcastmortem/episodes/15-Le-ML-Ops-pour-acclrer-linnovation--de-la-RD--la-production-e1mqve1/a-a8dssku", "authors": [], "publisher": { "name": "Spotify for Creators", "url": "https://creators.spotify.com" }, "datePublished": null, "access": "public", "schemaType": "PodcastEpisode", "preview": { "path": "/img/library/ref-3611e35f7814.webp", "width": 400, "height": 400, "sourcePage": "https://creators.spotify.com/pod/profile/podcastmortem/episodes/15-Le-ML-Ops-pour-acclrer-linnovation--de-la-RD--la-production-e1mqve1/a-a8dssku", "sourceImage": "https://d3t3ozftmdmh3i.cloudfront.net/staging/podcast_uploaded_episode400/8698156/07b49dd0696970d2.jpeg", "sourceWidth": 400, "sourceHeight": 400, "credit": "Spotify for Creators", "thumbnail": { "path": "/img/library/ref-3611e35f7814-small.webp", "width": 400, "height": 400 } } }, "summary": "A discussion of MLOps and the path from research to production.", "curation": { "note": null, "listingNote": "A discussion of MLOps and the path from research to production." }, "topics": [ "Product & design" ], "tags": [ "Product engineering" ], "related": [ { "id": "mlops", "reason": "Airflow offers concrete examples of coordinating machine-learning workflows." }, { "id": "how-we-built-an-ai-factory-part-1", "reason": "An AI-factory case study connects production practices with team organization." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/15-le-ml-ops-pour-accelerer-linnovation-de-la-r-d-a-la-production-by-post-mortem", "status": "curated", "citedIn": [ { "title": "Industrialiser la GenAI : Build vs Buy", "url": "https://fpaupier.fr/202501_productionize_genai" } ] }, { "id": "ilsvrc-2014-human-image-classification-interface", "slug": "ilsvrc-2014-human-image-classification-interface", "source": { "title": "ILSVRC 2014 human image-classification interface", "type": "website", "canonicalUrl": "https://cs.stanford.edu/people/karpathy/ilsvrc/", "authors": [ { "name": "Andrej Karpathy", "type": "Person" } ], "publisher": { "name": "Stanford University", "url": "https://cs.stanford.edu" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "sourcePage": "https://cs.stanford.edu/people/karpathy/ilsvrc/", "credit": "Andrej Karpathy / Stanford University", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "The ILSVRC labeling interface, showing a test photograph beside example images for candidate categories.", "sourceWidth": 1800, "sourceHeight": 1000, "path": "/img/library/ilsvrc-2014-human-image-classification-interface.webp", "width": 1200, "height": 667, "thumbnail": { "path": "/img/library/ilsvrc-2014-human-image-classification-interface-small.webp", "width": 480, "height": 267 } } }, "summary": "An interactive tool for comparing human image classification with ImageNet model predictions.", "curation": { "note": null, "listingNote": "An interactive tool for comparing human image classification with ImageNet model predictions." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "what-i-learned-from-competing-against-a-convnet-on-imagenet", "reason": "Karpathy’s account explains the human image-classification experiment behind this interface." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/ilsvrc-2014-human-image-classification-interface", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "carrying-out-error-analysis", "slug": "carrying-out-error-analysis", "source": { "title": "Carrying out error analysis", "type": "documentation", "canonicalUrl": "https://cs230.stanford.edu/files/C3M2.pdf", "authors": [ { "name": "Andrew Ng", "type": "Person" } ], "publisher": { "name": "DeepLearning.AI", "url": "https://cs230.stanford.edu" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://cs230.stanford.edu/files/C3M2.pdf", "sourceImage": "https://cs230.stanford.edu/files/C3M2.pdf#page=2", "credit": "Andrew Ng / DeepLearning.AI", "kind": "document-page", "pageNumber": 2, "alt": "Title slide for Carrying out error analysis, from the DeepLearning.AI Error Analysis lectures.", "sourceWidth": 1200, "sourceHeight": 675, "path": "/img/library/carrying-out-error-analysis.webp", "width": 1200, "height": 675, "thumbnail": { "path": "/img/library/carrying-out-error-analysis-small.webp", "width": 480, "height": 270 } } }, "summary": "Lecture slides on inspecting model errors to prioritize machine-learning improvements.", "curation": { "note": null, "listingNote": "Lecture slides on inspecting model errors to prioritize machine-learning improvements." }, "topics": [ "Product & design" ], "tags": [ "Product engineering" ], "related": [ { "id": "carrying-out-error-analysis-c3w2l01", "reason": "The video lecture presents the same error-analysis material in another format." }, { "id": "ai-product-evals", "reason": "Apply error-analysis principles to task-specific AI-product evaluations." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/carrying-out-error-analysis", "status": "curated" }, { "id": "trulens-pinecone-docs", "slug": "trulens-pinecone-docs", "source": { "title": "TruLens - Pinecone Docs", "type": "documentation", "canonicalUrl": "https://docs.pinecone.io/integrations/trulens", "authors": [], "publisher": { "name": "Pinecone Docs", "url": "https://docs.pinecone.io" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-46b7a9a1e480.webp", "width": 1200, "height": 630, "sourcePage": "https://docs.pinecone.io/integrations/trulens", "sourceImage": "https://www.pinecone.io/images/docs_og_image_v2.png", "sourceWidth": 1200, "sourceHeight": 630, "credit": "Pinecone Docs", "thumbnail": { "path": "/img/library/ref-46b7a9a1e480-small.webp", "width": 480, "height": 252 } } }, "summary": "Documentation on evaluating retrieval applications with TruLens and Pinecone.", "curation": { "note": null, "listingNote": "Documentation on evaluating retrieval applications with TruLens and Pinecone." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "trulens-evals-and-tracing-for-ai-agents", "reason": "The underlying TruLens project supplies the broader evaluation tooling." }, { "id": "the-rag-triad", "reason": "The RAG triad explains the checks used to evaluate retrieval applications." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/trulens-pinecone-docs", "status": "curated" }, { "id": "openai-compatible-server", "slug": "openai-compatible-server", "source": { "title": "OpenAI-Compatible Server", "type": "documentation", "canonicalUrl": "https://docs.vllm.ai/en/stable/serving/online_serving/openai_compatible_server/", "authors": [], "publisher": { "name": "vLLM", "url": "https://docs.vllm.ai" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://docs.vllm.ai/en/stable/serving/online_serving/openai_compatible_server/", "credit": "vLLM", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "vLLM documentation listing supported OpenAI-compatible APIs and server usage.", "sourceWidth": 688, "sourceHeight": 800, "path": "/img/library/openai-compatible-server.webp", "width": 688, "height": 800, "thumbnail": { "path": "/img/library/openai-compatible-server-small.webp", "width": 480, "height": 558 } } }, "summary": "Documentation for serving language models through an OpenAI-compatible API with vLLM.", "curation": { "note": null, "listingNote": "Documentation for serving language models through an OpenAI-compatible API with vLLM." }, "topics": [ "Engineering" ], "tags": [ "Inference" ], "related": [ { "id": "vllm-project-vllm", "reason": "The project repository provides the implementation behind this serving interface." }, { "id": "migrating-from-openai-to-open-llms-using-tgis-messages-api", "reason": "Compare another inference server’s approach to an OpenAI-compatible application interface." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/openai-compatible-server", "status": "curated" }, { "id": "mean-reciprocal-rank", "slug": "mean-reciprocal-rank", "source": { "title": "Mean reciprocal rank", "type": "documentation", "canonicalUrl": "https://en.wikipedia.org/wiki/Mean_reciprocal_rank", "authors": [], "publisher": { "name": "Wikipedia", "url": "https://en.wikipedia.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://en.wikipedia.org/wiki/Mean_reciprocal_rank", "credit": "Wikipedia contributors", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "Wikipedia explanation of mean reciprocal rank with the metric formula and a worked ranking example.", "sourceWidth": 972, "sourceHeight": 900, "path": "/img/library/mean-reciprocal-rank.webp", "width": 972, "height": 900, "thumbnail": { "path": "/img/library/mean-reciprocal-rank-small.webp", "width": 480, "height": 444 } } }, "summary": "An explanation of mean reciprocal rank as a retrieval metric.", "curation": { "note": null, "listingNote": "An explanation of mean reciprocal rank as a retrieval metric." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "discounted-cumulative-gain", "reason": "Compare reciprocal rank with a metric that considers relevance across a ranked list." }, { "id": "introduction-information-retrieval", "reason": "The retrieval textbook explains how ranking metrics fit into system evaluation." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/mean-reciprocal-rank", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "prompting-fundamentals-and-how-to-apply-them-effectively", "slug": "prompting-fundamentals-and-how-to-apply-them-effectively", "source": { "title": "Prompting Fundamentals and How to Apply them Effectively", "type": "article", "canonicalUrl": "https://eugeneyan.com/writing/prompting/", "authors": [ { "name": "Eugene Yan", "type": "Person" } ], "publisher": { "name": "eugeneyan.com", "url": "https://eugeneyan.com" }, "datePublished": "2024-05-26", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-4be225b4f176.webp", "width": 1200, "height": 630, "sourcePage": "https://eugeneyan.com/writing/prompting/", "sourceImage": "https://eugeneyan.com/assets/og_image/prompting.jpg", "sourceWidth": 1200, "sourceHeight": 630, "credit": "eugeneyan.com", "thumbnail": { "path": "/img/library/ref-4be225b4f176-small.webp", "width": 480, "height": 252 } } }, "summary": "Prompting techniques grounded in how language models respond to instructions and examples.", "curation": { "note": null, "listingNote": "Prompting techniques grounded in how language models respond to instructions and examples." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "using-reasoning-models-differently-from-chat-models", "reason": "Reasoning models motivate different usage patterns from conventional chat prompting." }, { "id": "applied-llms", "reason": "Practitioner experience puts prompting alongside retrieval and evaluation work." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/prompting-fundamentals-and-how-to-apply-them-effectively", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "apple-aiml-research-ml-ferret", "slug": "apple-aiml-research-ml-ferret", "source": { "title": "apple-aiml-research/ml-ferret", "type": "repository", "canonicalUrl": "https://github.com/apple-aiml-research/ml-ferret", "authors": [], "publisher": { "name": "GitHub", "url": "https://github.com" }, "datePublished": null, "access": "public", "schemaType": "SoftwareSourceCode", "preview": { "path": "/img/library/ref-ebaca18cb3b3.webp", "width": 1200, "height": 600, "sourcePage": "https://github.com/apple-aiml-research/ml-ferret", "sourceImage": "https://opengraph.githubassets.com/728e1873981f880ae4827352873d4109a4c632dcdd9800c1d4ba731055f951c1/apple-aiml-research/ml-ferret", "sourceWidth": 1200, "sourceHeight": 600, "credit": "GitHub", "thumbnail": { "path": "/img/library/ref-ebaca18cb3b3-small.webp", "width": 480, "height": 240 } } }, "summary": "Apple’s research code for the Ferret multimodal model.", "curation": { "note": null, "listingNote": "Apple’s research code for the Ferret multimodal model." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "learning-transferable-visual-models-from-natural-language-supervision", "reason": "CLIP provides background on linking images with language representations." }, { "id": "multimodal-inputs", "reason": "Explore how multimodal inputs are represented when serving models." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/apple-aiml-research-ml-ferret", "status": "curated" }, { "id": "open-webui-open-webui", "slug": "open-webui-open-webui", "source": { "title": "open-webui/open-webui", "type": "repository", "canonicalUrl": "https://github.com/open-webui/open-webui", "authors": [], "publisher": { "name": "GitHub", "url": "https://github.com" }, "datePublished": null, "access": "public", "schemaType": "SoftwareSourceCode", "preview": { "path": "/img/library/ref-e7a3efa20968.webp", "width": 1200, "height": 600, "sourcePage": "https://github.com/open-webui/open-webui", "sourceImage": "https://repository-images.githubusercontent.com/701547123/275da62f-2139-4d2b-bcac-57c82872dd30", "sourceWidth": 1280, "sourceHeight": 640, "credit": "GitHub", "thumbnail": { "path": "/img/library/ref-e7a3efa20968-small.webp", "width": 480, "height": 240 } } }, "summary": "A user interface for working with language models through local or hosted services.", "curation": { "note": null, "listingNote": "A user interface for working with language models through local or hosted services." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "ollama-run-ai-models-locally-and-in-the-cloud", "reason": "Ollama is another part of a locally hosted language-model workflow." }, { "id": "lm-studio-bionic-your-agent-for-work-and-code", "reason": "Compare a second interface and toolset for working with local models." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/open-webui-open-webui", "status": "curated" }, { "id": "gen-ai-at-work-the-story-of-carrefour-marketing-studio-horizons-by-carrefour", "slug": "gen-ai-at-work-the-story-of-carrefour-marketing-studio-horizons-by-carrefour", "source": { "title": "Gen AI at work: The story of Carrefour Marketing Studio - Horizons by Carrefour", "type": "article", "canonicalUrl": "https://horizons.carrefour.com/data/gen-ai-at-work-the-story-of-carrefour-marketing-studio", "authors": [ { "name": "Hélène Labaume", "type": "Person" }, { "name": "Alaa El Helw", "type": "Person" } ], "publisher": { "name": "Carrefour", "url": "https://horizons.carrefour.com" }, "datePublished": "2024-02-01", "access": "public", "schemaType": "Article", "availability": "unavailable", "preview": { "sourcePage": "https://web.archive.org/web/20241013063737/https://horizons.carrefour.com/data/gen-ai-at-work-the-story-of-carrefour-marketing-studio", "sourceImage": "https://storage.googleapis.com/c4-horizon-media/2024/01/image8c.jpg", "credit": "Carrefour / Horizons", "kind": "source-image", "alt": "Carrefour demonstration stand shown in its Marketing Studio article.", "sourceWidth": 1993, "sourceHeight": 1340, "path": "/img/library/gen-ai-at-work-the-story-of-carrefour-marketing-studio-horizons-by-carrefour.webp", "width": 1200, "height": 807, "thumbnail": { "path": "/img/library/gen-ai-at-work-the-story-of-carrefour-marketing-studio-horizons-by-carrefour-small.webp", "width": 480, "height": 323 } } }, "summary": "Carrefour’s account of developing a generative-AI studio for marketing teams.", "curation": { "note": null, "listingNote": "Carrefour’s account of developing a generative-AI studio for marketing teams." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "rex-ia-generative-avec-carrefourfrance-iagenerative", "reason": "A video discussion covers Carrefour’s experience applying generative AI." }, { "id": "how-people-create-and-destroy-value-with-generative-ai", "reason": "Compare the deployment story with research into AI’s effects on knowledge work." } ], "archive": { "snapshotUrl": "https://web.archive.org/web/20241013063737/https://horizons.carrefour.com/data/gen-ai-at-work-the-story-of-carrefour-marketing-studio" }, "detailUrl": "https://fpaupier.fr/library/gen-ai-at-work-the-story-of-carrefour-marketing-studio-horizons-by-carrefour", "status": "curated" }, { "id": "mistralai-mistral-small-3-1-24b-instruct-2503", "slug": "mistralai-mistral-small-3-1-24b-instruct-2503", "source": { "title": "mistralai/Mistral-Small-3.1-24B-Instruct-2503", "type": "website", "canonicalUrl": "https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-f0e93120d69e.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/models/mistralai/Mistral-Small-3.1-24B-Instruct-2503.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-f0e93120d69e-small.webp", "width": 480, "height": 259 } } }, "summary": "The model card for Mistral Small 3.1 24B Instruct 2503.", "curation": { "note": null, "listingNote": "The model card for Mistral Small 3.1 24B Instruct 2503." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "mistral-small-3-1-mistral-ai", "reason": "The release announcement explains the model’s place in Mistral’s product family." }, { "id": "mistralai-mistral-small-3-1-24b-base-2503", "reason": "Compare the instruction-tuned model with the corresponding base model." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/mistralai-mistral-small-3-1-24b-instruct-2503", "status": "curated" }, { "id": "building-a-generative-ai-platform", "slug": "building-a-generative-ai-platform", "source": { "title": "Building A Generative AI Platform", "type": "article", "canonicalUrl": "https://huyenchip.com/2024/07/25/genai-platform.html", "authors": [ { "name": "Chip Huyen", "type": "Person" } ], "publisher": { "name": "Chip Huyen", "url": "https://huyenchip.com" }, "datePublished": "2024-07-25", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-1eed397e84d5.webp", "width": 1200, "height": 678, "sourcePage": "https://huyenchip.com/2024/07/25/genai-platform.html", "sourceImage": "https://huyenchip.com/assets/pics/genai-platform/1-genai-platform.png", "sourceWidth": 1999, "sourceHeight": 1129, "credit": "Chip Huyen", "thumbnail": { "path": "/img/library/ref-1eed397e84d5-small.webp", "width": 480, "height": 271 } } }, "summary": "Architectural choices and components involved in building a generative-AI platform.", "curation": { "note": null, "listingNote": "Architectural choices and components involved in building a generative-AI platform." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "ai-engineer-2023-keynote-building-blocks-for-llm-systems", "reason": "A complementary view of the building blocks used in LLM systems." }, { "id": "hidden-technical-debt", "reason": "Technical-debt research highlights the dependencies introduced by platform decisions." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/building-a-generative-ai-platform", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "rag-is-more-than-just-embedding-search", "slug": "rag-is-more-than-just-embedding-search", "source": { "title": "RAG is more than just embedding search", "type": "article", "canonicalUrl": "https://jxnl.co/writing/2023/09/17/rag-is-more-than-embeddings/", "authors": [ { "name": "Jason Liu", "type": "Person" } ], "publisher": { "name": "jxnl.co", "url": "https://jxnl.co" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-83e5a7a5c152.webp", "width": 1200, "height": 630, "sourcePage": "https://jxnl.co/writing/2023/09/17/rag-is-more-than-embeddings/", "sourceImage": "https://jxnl.co/assets/images/social/writing/2023/09/17/rag-is-more-than-embeddings.png", "sourceWidth": 1200, "sourceHeight": 630, "credit": "jxnl.co", "thumbnail": { "path": "/img/library/ref-83e5a7a5c152-small.webp", "width": 480, "height": 252 } } }, "summary": "Why retrieval systems need more than a vector search over embeddings.", "curation": { "note": null, "listingNote": "Why retrieval systems need more than a vector search over embeddings." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "introduction-information-retrieval", "reason": "Classical retrieval foundations explain why embeddings alone are insufficient." }, { "id": "improved-text-scoring-with-bm25", "reason": "BM25 supplies a lexical ranking approach to compare with vector retrieval." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/rag-is-more-than-just-embedding-search", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "stop-using-lgtm-few-as-a-metric-better-rag", "slug": "stop-using-lgtm-few-as-a-metric-better-rag", "source": { "title": "Stop using LGTM@Few as a metric (Better RAG)", "type": "article", "canonicalUrl": "https://jxnl.co/writing/2024/02/05/when-to-lgtm-at-k/", "authors": [ { "name": "Jason Liu", "type": "Person" } ], "publisher": { "name": "jxnl.co", "url": "https://jxnl.co" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-a1686dfa1aa6.webp", "width": 1200, "height": 630, "sourcePage": "https://jxnl.co/writing/2024/02/05/when-to-lgtm-at-k/#understanding-k", "sourceImage": "https://jxnl.co/assets/images/social/writing/2024/02/05/when-to-lgtm-at-k.png", "sourceWidth": 1200, "sourceHeight": 630, "credit": "jxnl.co", "thumbnail": { "path": "/img/library/ref-a1686dfa1aa6-small.webp", "width": 480, "height": 252 } } }, "summary": "Why inspecting only a few retrieval results is a weak evaluation method.", "curation": { "note": null, "listingNote": "Why inspecting only a few retrieval results is a weak evaluation method." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "ragas-automated-evaluation-of-retrieval-augmented-generation", "reason": "Ragas offers a more systematic approach to evaluating retrieval-augmented generation." }, { "id": "generate-synthetic-data-for-evaluating-rag-systems-using-amazon-bedrock", "reason": "Synthetic test data is one way to expand beyond inspecting a few examples." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/stop-using-lgtm-few-as-a-metric-better-rag", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "what-i-learned-from-competing-against-a-convnet-on-imagenet", "slug": "what-i-learned-from-competing-against-a-convnet-on-imagenet", "source": { "title": "What I learned from competing against a ConvNet on ImageNet", "type": "article", "canonicalUrl": "https://karpathy.github.io/2014/09/02/what-i-learned-from-competing-against-a-convnet-on-imagenet/", "authors": [ { "name": "Andrej Karpathy", "type": "Person" } ], "publisher": { "name": "karpathy.github.io", "url": "https://karpathy.github.io" }, "datePublished": "2014-09-02", "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://karpathy.github.io/2014/09/02/what-i-learned-from-competing-against-a-convnet-on-imagenet/", "sourceImage": "https://karpathy.github.io/assets/cnntsne.jpeg", "credit": "Andrej Karpathy / ImageNet", "kind": "source-image", "alt": "A mosaic of example ImageNet classification images grouped by visual similarity.", "sourceWidth": 799, "sourceHeight": 300, "path": "/img/library/what-i-learned-from-competing-against-a-convnet-on-imagenet.webp", "width": 799, "height": 300, "thumbnail": { "path": "/img/library/what-i-learned-from-competing-against-a-convnet-on-imagenet-small.webp", "width": 480, "height": 180 }, "caption": "Example images from the classification task" } }, "summary": "Lessons from comparing human image recognition with a convolutional neural network.", "curation": { "note": null, "listingNote": "Lessons from comparing human image recognition with a convolutional neural network." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "ilsvrc-2014-human-image-classification-interface", "reason": "Try the human image-classification interface that accompanies this experiment." }, { "id": "ultralytics-ultralytics", "reason": "A practical computer-vision codebase complements the account of recognition performance." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/what-i-learned-from-competing-against-a-convnet-on-imagenet", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "extrinsic-hallucinations-in-llms", "slug": "extrinsic-hallucinations-in-llms", "source": { "title": "Extrinsic Hallucinations in LLMs", "type": "article", "canonicalUrl": "https://lilianweng.github.io/posts/2024-07-07-hallucination/", "authors": [ { "name": "Lilian Weng", "type": "Person" } ], "publisher": { "name": "lilianweng.github.io", "url": "https://lilianweng.github.io" }, "datePublished": "2024-07-07", "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://lilianweng.github.io/posts/2024-07-07-hallucination/", "credit": "Wei et al. (2024), reproduced in Lilian Weng’s article", "kind": "source-image", "alt": "SAFE workflow checks individual facts in a language-model response against retrieved evidence", "sourceWidth": 3596, "sourceHeight": 3244, "path": "/img/library/extrinsic-hallucinations-in-llms.webp", "width": 1200, "height": 1083, "thumbnail": { "path": "/img/library/extrinsic-hallucinations-in-llms-small.webp", "width": 480, "height": 433 }, "sourceImage": "https://lilianweng.github.io/posts/2024-07-07-hallucination/SAFE-overview.png" } }, "summary": "A survey of hallucination mechanisms and mitigation approaches in language models.", "curation": { "note": null, "listingNote": "A survey of hallucination mechanisms and mitigation approaches in language models." }, "topics": [ "AI & machine learning" ], "tags": [ "Evaluation" ], "related": [ { "id": "contextcite", "reason": "Context attribution offers one way to investigate support for generated statements." }, { "id": "from-single-to-multi-how-llms-hallucinate-in-multi-document-summarization", "reason": "A focused study examines hallucinations in multi-document summarization." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/extrinsic-hallucinations-in-llms", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "lm-studio-bionic-your-agent-for-work-and-code", "slug": "lm-studio-bionic-your-agent-for-work-and-code", "source": { "title": "LM Studio Bionic - Your Agent for Work and Code", "type": "website", "canonicalUrl": "https://lmstudio.ai/", "authors": [], "publisher": { "name": "lmstudio.ai", "url": "https://lmstudio.ai" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-14772d17b277.webp", "width": 1200, "height": 630, "sourcePage": "https://lmstudio.ai/", "sourceImage": "https://files.lmstudio.ai/bionic/lm-studio-bionic-og.jpg", "sourceWidth": 1200, "sourceHeight": 630, "credit": "lmstudio.ai", "thumbnail": { "path": "/img/library/ref-14772d17b277-small.webp", "width": 480, "height": 252 } } }, "summary": "Software and documentation for working with locally hosted language models.", "curation": { "note": null, "listingNote": "Software and documentation for working with locally hosted language models." }, "topics": [ "AI & machine learning" ], "tags": [ "Agents" ], "related": [ { "id": "ollama-run-ai-models-locally-and-in-the-cloud", "reason": "Compare a second runtime for working with locally hosted models." }, { "id": "open-webui-open-webui", "reason": "An alternative interface for local or hosted language-model services." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/lm-studio-bionic-your-agent-for-work-and-code", "status": "curated" }, { "id": "ahead-of-ai-sebastian-raschka-phd-substack", "slug": "ahead-of-ai-sebastian-raschka-phd-substack", "source": { "title": "Ahead of AI | Sebastian Raschka, PhD | Substack", "type": "website", "canonicalUrl": "https://magazine.sebastianraschka.com/", "authors": [ { "name": "Sebastian Raschka, PhD", "type": "Person" } ], "publisher": { "name": "magazine.sebastianraschka.com", "url": "https://magazine.sebastianraschka.com" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-334a0dbc92a4.webp", "width": 400, "height": 400, "sourcePage": "https://magazine.sebastianraschka.com/", "sourceImage": "https://substackcdn.com/image/fetch/$s_!96vs!,w_400,h_400,c_fill,f_auto,q_auto:good,fl_progressive:steep,g_auto/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F49f25d0a-212b-4853-8bcb-128d0a3edbbf_1196x1196.png", "sourceWidth": 400, "sourceHeight": 400, "credit": "magazine.sebastianraschka.com", "thumbnail": { "path": "/img/library/ref-334a0dbc92a4-small.webp", "width": 400, "height": 400 } } }, "summary": "Sebastian Raschka’s writing on machine learning and AI research.", "curation": { "note": null, "listingNote": "Sebastian Raschka’s writing on machine learning and AI research." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "practical-tips-for-finetuning-llms-using-lora-low-rank-adaptation", "reason": "A focused fine-tuning article from Sebastian Raschka’s broader machine-learning writing." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/ahead-of-ai-sebastian-raschka-phd-substack", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "how-we-built-an-ai-factory-part-1", "slug": "how-we-built-an-ai-factory-part-1", "source": { "title": "How we built an AI factory: Part 1", "type": "article", "canonicalUrl": "https://medium.com/earthcube-stories/how-we-built-an-ai-factory-part-1-2fb34c4cc648", "authors": [ { "name": "Marie-Fleur Sacreste", "type": "Person" } ], "publisher": { "name": "Safran.AI Stories", "url": "https://medium.com" }, "datePublished": "2021-08-31", "access": "restricted", "schemaType": "Article", "preview": { "sourcePage": "https://medium.com/earthcube-stories/how-we-built-an-ai-factory-part-1-2fb34c4cc648", "sourceImage": "https://cdn-images-1.medium.com/max/1014/0*SFkF7NkTcZAKkF_W", "credit": "Preligens / Safran.AI Stories", "kind": "source-image", "alt": "Preligens diagram comparing a road detector output before and after post-processing.", "sourceWidth": 1014, "sourceHeight": 379, "path": "/img/library/how-we-built-an-ai-factory-part-1.webp", "width": 1014, "height": 379, "thumbnail": { "path": "/img/library/how-we-built-an-ai-factory-part-1-small.webp", "width": 480, "height": 179 }, "caption": "Original diagram showing the effect of post-processing" } }, "summary": "How Preligens structured the frameworks, teams, and production practices behind its AI factory.", "curation": { "note": null, "listingNote": "How Preligens structured the frameworks, teams, and production practices behind its AI factory." }, "topics": [ "Product & design" ], "tags": [ "Product engineering" ], "related": [ { "id": "15-le-ml-ops-pour-accelerer-linnovation-de-la-r-d-a-la-production-by-post-mortem", "reason": "A complementary discussion of moving machine-learning work from research to production." }, { "id": "building-a-generative-ai-platform", "reason": "Compare the AI-factory case study with generative-AI platform architecture." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/how-we-built-an-ai-factory-part-1", "status": "curated", "citedIn": [ { "title": "Industrialiser la GenAI : Build vs Buy", "url": "https://fpaupier.fr/202501_productionize_genai" } ] }, { "id": "switching-perspectives-on-generative-artificial-intelligence-a-design-view-for-humans", "slug": "switching-perspectives-on-generative-artificial-intelligence-a-design-view-for-humans", "source": { "title": "Switching perspectives on generative artificial intelligence: a design view for humans-generative AI co-creativity", "type": "paper", "canonicalUrl": "https://minesparis-psl.hal.science/hal-04520521v1", "authors": [ { "name": "Antoine Bordas", "type": "Person" }, { "name": "Pascal Le Masson", "type": "Person" }, { "name": "Benoit Weil", "type": "Person" } ], "publisher": { "name": "HAL", "url": "https://minesparis-psl.hal.science" }, "datePublished": "2024-06-17", "access": "public", "schemaType": "ScholarlyArticle", "preview": { "sourcePage": "https://minesparis-psl.hal.science/hal-04520521v1", "sourceImage": "https://web.archive.org/web/20260509140252id_/https://minesparis-psl.hal.science/hal-04520521v1/document", "credit": "Antoine Bordas, Pascal Le Masson, Benoit Weil", "kind": "document-page", "alt": "Title page of Switching perspectives on generative artificial intelligence: a design view for humans-generative AI co-creativity.", "sourceWidth": 1272, "sourceHeight": 1800, "path": "/img/library/switching-perspectives-on-generative-artificial-intelligence-a-design-view-for-humans.webp", "width": 1131, "height": 1600, "thumbnail": { "path": "/img/library/switching-perspectives-on-generative-artificial-intelligence-a-design-view-for-humans-small.webp", "width": 452, "height": 640 }, "pageNumber": 2, "caption": "First page of the paper in the HAL manuscript, retrieved from the Internet Archive." } }, "summary": "A design perspective on collaboration between humans and generative AI.", "curation": { "note": null, "listingNote": "A design perspective on collaboration between humans and generative AI." }, "topics": [ "Product & design" ], "tags": [ "Product engineering" ], "related": [ { "id": "how-people-create-and-destroy-value-with-generative-ai", "reason": "Empirical knowledge-work results complement the design perspective on co-creation." }, { "id": "your-success-with-generative-ai-may-come-down-to-these-ux-decisions", "reason": "Interface choices translate human-AI collaboration into product behavior." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/switching-perspectives-on-generative-artificial-intelligence-a-design-view-for-humans", "status": "curated", "citedIn": [ { "title": "Industrialiser la GenAI : Build vs Buy", "url": "https://fpaupier.fr/202501_productionize_genai" } ] }, { "id": "mistral-small-3-1-mistral-ai", "slug": "mistral-small-3-1-mistral-ai", "source": { "title": "Mistral Small 3.1 | Mistral AI", "type": "article", "canonicalUrl": "https://mistral.ai/news/mistral-small-3-1/", "authors": [], "publisher": { "name": "Mistral", "url": "https://mistral.ai" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-945c0110291e.webp", "width": 1200, "height": 716, "sourcePage": "https://mistral.ai/news/mistral-small-3-1/", "sourceImage": "https://mistral.ai/cms-media/api/media/file/Thumbnail-Model-Small.jpg", "sourceWidth": 1800, "sourceHeight": 1074, "credit": "Mistral", "thumbnail": { "path": "/img/library/ref-945c0110291e-small.webp", "width": 480, "height": 286 } } }, "summary": "Mistral’s announcement of the Small 3.1 model.", "curation": { "note": null, "listingNote": "Mistral’s announcement of the Small 3.1 model." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "mistralai-mistral-small-3-1-24b-instruct-2503", "reason": "The model card documents the instruction-tuned release announced here." }, { "id": "mistralai-mistral-small-3-1-24b-base-2503", "reason": "The base-model card gives another view of the same model family." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/mistral-small-3-1-mistral-ai", "status": "curated" }, { "id": "ollama-run-ai-models-locally-and-in-the-cloud", "slug": "ollama-run-ai-models-locally-and-in-the-cloud", "source": { "title": "Ollama · Run AI models locally and in the cloud", "type": "website", "canonicalUrl": "https://ollama.com/", "authors": [], "publisher": { "name": "ollama.com", "url": "https://ollama.com" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-faeb9190175b.webp", "width": 1200, "height": 630, "sourcePage": "https://ollama.com/", "sourceImage": "https://ollama.com/public/og.png", "sourceWidth": 1200, "sourceHeight": 630, "credit": "ollama.com", "thumbnail": { "path": "/img/library/ref-faeb9190175b-small.webp", "width": 480, "height": 252 } } }, "summary": "A runtime for working with language models locally.", "curation": { "note": null, "listingNote": "A runtime for working with language models locally." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "lm-studio-bionic-your-agent-for-work-and-code", "reason": "Compare another local-model toolset and interface." }, { "id": "the-diminishing-returns-of-deploying-local-large-language-models", "reason": "A critical account examines the trade-offs of running models locally." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/ollama-run-ai-models-locally-and-in-the-cloud", "status": "curated" }, { "id": "hello-gpt-4o", "slug": "hello-gpt-4o", "source": { "title": "Hello GPT-4o", "type": "article", "canonicalUrl": "https://openai.com/index/hello-gpt-4o/", "authors": [], "publisher": { "name": "OpenAI", "url": "https://openai.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-c5238fa1e012.webp", "width": 1200, "height": 675, "sourcePage": "https://openai.com/index/hello-gpt-4o/", "sourceImage": "https://images.ctfassets.net/kftzwdyauwt9/4hxowKfhJ6Q3bVMqGsosjS/97103375a2334386e2f2e66bc648664a/GPT-4o.png?w=1600&h=900&fit=fill", "sourceWidth": 1600, "sourceHeight": 900, "credit": "OpenAI", "thumbnail": { "path": "/img/library/ref-c5238fa1e012-small.webp", "width": 480, "height": 270 } } }, "summary": "OpenAI’s introduction to GPT-4o and its multimodal capabilities.", "curation": { "note": null, "listingNote": "OpenAI’s introduction to GPT-4o and its multimodal capabilities." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "sharegpt-4o", "reason": "A dataset of GPT-4o multimodal annotations illustrates a downstream use." }, { "id": "learning-transferable-visual-models-from-natural-language-supervision", "reason": "CLIP provides earlier research context for joining vision and language." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/hello-gpt-4o", "status": "curated" }, { "id": "opentelemetry", "slug": "opentelemetry", "source": { "title": "OpenTelemetry", "type": "website", "canonicalUrl": "https://opentelemetry.io/", "authors": [], "publisher": { "name": "OpenTelemetry", "url": "https://opentelemetry.io" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "sourcePage": "https://opentelemetry.io/", "credit": "OpenTelemetry", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "OpenTelemetry project homepage introducing the open standard for telemetry.", "sourceWidth": 1200, "sourceHeight": 850, "path": "/img/library/opentelemetry.webp", "width": 1200, "height": 850, "thumbnail": { "path": "/img/library/opentelemetry-small.webp", "width": 480, "height": 340 } } }, "summary": "The open observability project covering traces, metrics, and logs.", "curation": { "note": null, "listingNote": "The open observability project covering traces, metrics, and logs." }, "topics": [ "Engineering" ], "tags": [ "Observability" ], "related": [ { "id": "traceloop-openllmetry", "reason": "OpenLLMetry applies OpenTelemetry instrumentation to generative-AI applications." }, { "id": "observability-data-model-langfuse", "reason": "Compare the general observability model with Langfuse’s traces and observations." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/opentelemetry", "status": "curated" }, { "id": "llm-evals-and-benchmarking-hackerllama", "slug": "llm-evals-and-benchmarking-hackerllama", "source": { "title": "LLM Evals and Benchmarking – hackerllama", "type": "article", "canonicalUrl": "https://osanseviero.github.io/hackerllama/blog/posts/llm_evals/", "authors": [], "publisher": { "name": "hackerllama", "url": "https://osanseviero.github.io" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-4e93a0b5998f.webp", "width": 1200, "height": 525, "sourcePage": "https://osanseviero.github.io/hackerllama/blog/posts/llm_evals/", "sourceImage": "https://osanseviero.github.io/hackerllama/blog/posts/llm_evals/llm_leaderboard.png", "sourceWidth": 1743, "sourceHeight": 763, "credit": "hackerllama", "thumbnail": { "path": "/img/library/ref-4e93a0b5998f-small.webp", "width": 480, "height": 210 } } }, "summary": "An introduction to language-model evaluation and benchmarking.", "curation": { "note": null, "listingNote": "An introduction to language-model evaluation and benchmarking." }, "topics": [ "AI & machine learning" ], "tags": [ "Evaluation" ], "related": [ { "id": "beyond-benchmarks-2024", "reason": "Look beyond conventional benchmark scores when comparing model performance." }, { "id": "llm-as-judge", "reason": "LLM-as-a-judge research examines one common evaluation approach." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/llm-evals-and-benchmarking-hackerllama", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "speed-matters", "slug": "speed-matters", "source": { "title": "Speed Matters", "type": "article", "canonicalUrl": "https://research.google/blog/speed-matters/", "authors": [], "publisher": { "name": "research.google", "url": "https://research.google" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-b7cd12e2fa33.webp", "width": 800, "height": 444, "sourcePage": "https://research.google/blog/speed-matters/", "sourceImage": "https://storage.googleapis.com/gweb-research2023-media/images/53f67c7f3ba7c094ad0b59ab3f986902-g.width-800.format-jpeg.jpg", "sourceWidth": 800, "sourceHeight": 444, "credit": "research.google", "thumbnail": { "path": "/img/library/ref-b7cd12e2fa33-small.webp", "width": 480, "height": 266 } } }, "summary": "Research into how web latency affects user behavior.", "curation": { "note": null, "listingNote": "Research into how web latency affects user behavior." }, "topics": [ "Engineering" ], "tags": [ "Inference" ], "related": [ { "id": "llm-inference-performance", "reason": "Translate the importance of latency into language-model serving engineering." }, { "id": "best-practices-for-llm-inference-performance-monitoring", "reason": "Monitoring practices help measure the inference behavior users experience." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/speed-matters", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "topic-extraction-with-non-negative-matrix-factorization-and-latent-dirichlet-allocati", "slug": "topic-extraction-with-non-negative-matrix-factorization-and-latent-dirichlet-allocati", "source": { "title": "Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation", "type": "documentation", "canonicalUrl": "https://scikit-learn.org/stable/auto_examples/applications/plot_topics_extraction_with_nmf_lda.html", "authors": [], "publisher": { "name": "scikit-learn", "url": "https://scikit-learn.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-1538363d8558.webp", "width": 277, "height": 150, "sourcePage": "https://scikit-learn.org/stable/auto_examples/applications/plot_topics_extraction_with_nmf_lda.html", "sourceImage": "https://scikit-learn.org/stable/_static/scikit-learn-logo-notext.png", "sourceWidth": 277, "sourceHeight": 150, "credit": "scikit-learn", "thumbnail": { "path": "/img/library/ref-1538363d8558-small.webp", "width": 277, "height": 150 } } }, "summary": "A worked example of extracting topics with NMF and latent Dirichlet allocation.", "curation": { "note": null, "listingNote": "A worked example of extracting topics with NMF and latent Dirichlet allocation." }, "topics": [ "Engineering" ], "tags": [ "Observability" ], "related": [ { "id": "latentdirichletallocation", "reason": "The API reference documents the LDA estimator used in this example." }, { "id": "bertopic", "reason": "Compare classical topic extraction with an embedding-based topic-modeling library." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/topic-extraction-with-non-negative-matrix-factorization-and-latent-dirichlet-allocati", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "llm-price-comparison", "slug": "llm-price-comparison", "source": { "title": "LLM Price Comparison", "type": "article", "canonicalUrl": "https://simmering.dev/blog/llm-price-performance/", "authors": [ { "name": "Paul Simmering", "type": "Person" } ], "publisher": { "name": "simmering.dev", "url": "https://simmering.dev" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://simmering.dev/blog/llm-price-performance/", "credit": "simmering.dev", "kind": "screenshot", "alt": "Screenshot of LLM Price Comparison", "sourceWidth": 1200, "sourceHeight": 780, "path": "/img/library/llm-price-comparison.webp", "width": 1200, "height": 780, "thumbnail": { "path": "/img/library/llm-price-comparison-small.webp", "width": 480, "height": 312 }, "capturedAt": "2026-10-13" } }, "summary": "A comparison of language-model price and performance.", "curation": { "note": null, "listingNote": "A comparison of language-model price and performance." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "llm-api-pricing-calculator", "reason": "Use a cost calculator alongside the price-performance comparison." }, { "id": "finding-the-right-llm-for-your-business-a-practical-cost-benefit-analysis", "reason": "Extend numerical comparisons into a business-oriented model-selection framework." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/llm-price-comparison", "status": "curated" }, { "id": "imitation-intelligence-my-keynote-for-pycon-us-2024", "slug": "imitation-intelligence-my-keynote-for-pycon-us-2024", "source": { "title": "Imitation Intelligence, my keynote for PyCon US 2024", "type": "article", "canonicalUrl": "https://simonwillison.net/2024/Jul/14/pycon/", "authors": [ { "name": "Simon Willison", "type": "Person" } ], "publisher": { "name": "Simon Willison’s Weblog", "url": "https://simonwillison.net" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-d24105ebcb53.webp", "width": 1200, "height": 675, "sourcePage": "https://simonwillison.net/2024/Jul/14/pycon/", "sourceImage": "https://static.simonwillison.net/static/2024/simonw-pycon-2024/simonw-pycon-2024.001.jpeg", "sourceWidth": 1920, "sourceHeight": 1080, "credit": "Simon Willison’s Weblog", "thumbnail": { "path": "/img/library/ref-d24105ebcb53-small.webp", "width": 480, "height": 270 } } }, "summary": "Simon Willison’s PyCon keynote on large language models and practical software development.", "curation": { "note": null, "listingNote": "Simon Willison’s PyCon keynote on large language models and practical software development." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "the-70-problem-hard-truths-about-ai-assisted-coding", "reason": "A focused account examines the remaining work in AI-assisted coding." }, { "id": "1hr-talk-intro-to-large-language-models", "reason": "A technical introduction complements the keynote’s practitioner perspective." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/imitation-intelligence-my-keynote-for-pycon-us-2024", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "websockets-vs-server-sent-events-eventsource", "slug": "websockets-vs-server-sent-events-eventsource", "source": { "title": "WebSockets vs. Server-Sent events/EventSource", "type": "documentation", "canonicalUrl": "https://stackoverflow.com/questions/5195452/websockets-vs-server-sent-events-eventsource", "authors": [], "publisher": { "name": "Stack Overflow", "url": "https://stackoverflow.com" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://stackoverflow.com/questions/5195452/websockets-vs-server-sent-events-eventsource", "credit": "Stack Overflow contributors", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "Stack Overflow discussion comparing WebSockets with server-sent events and their use cases.", "sourceWidth": 663, "sourceHeight": 920, "path": "/img/library/websockets-vs-server-sent-events-eventsource.webp", "width": 663, "height": 920, "thumbnail": { "path": "/img/library/websockets-vs-server-sent-events-eventsource-small.webp", "width": 461, "height": 640 } } }, "summary": "A technical discussion comparing bidirectional sockets with one-way browser event streams.", "curation": { "note": null, "listingNote": "A technical discussion comparing bidirectional sockets with one-way browser event streams." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "building-a-generative-ai-platform", "reason": "Streaming transport is an implementation choice within a generative-AI platform." }, { "id": "web-operations-keeping-the-data-on-time", "reason": "Operational practices provide context for maintaining networked applications." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/websockets-vs-server-sent-events-eventsource", "status": "curated" }, { "id": "intel-announces-launch-of-enterprise-gen-ai-spinoff-articul8", "slug": "intel-announces-launch-of-enterprise-gen-ai-spinoff-articul8", "source": { "title": "Intel Announces Launch of Enterprise Gen AI Spinoff Articul8", "type": "article", "canonicalUrl": "https://technologymagazine.com/articles/intel-launches-enterprise-gen-ai-spinoff-articul8", "authors": [], "publisher": { "name": "technologymagazine.com", "url": "https://technologymagazine.com" }, "datePublished": "2024-01-03", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-1769b0607bec.webp", "width": 1200, "height": 631, "sourcePage": "https://technologymagazine.com/articles/intel-launches-enterprise-gen-ai-spinoff-articul8", "sourceImage": "https://assets.bizclikmedia.net/1200/14583f04a364cdf5c46199ccd5bf4e6d:2044204ea7595f56d640d6319d9052ab/intel-hq.jpg.jpg", "sourceWidth": 1200, "sourceHeight": 631, "credit": "technologymagazine.com", "thumbnail": { "path": "/img/library/ref-1769b0607bec-small.webp", "width": 480, "height": 252 } } }, "summary": "Reporting on Intel’s enterprise generative-AI spinout, Articul8.", "curation": { "note": null, "listingNote": "Reporting on Intel’s enterprise generative-AI spinout, Articul8." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "what-is-a-corporate-spin-off", "reason": "The corporate spin-off reference explains the organizational form of Articul8." }, { "id": "safran-buys-ai-firm-preligens-for-220-million-euros", "reason": "Compare another AI-company transaction in an established industrial group." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/intel-announces-launch-of-enterprise-gen-ai-spinoff-articul8", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "autonomy-in-weapon-systems-dod-directive-3000-09", "slug": "autonomy-in-weapon-systems-dod-directive-3000-09", "source": { "title": "Autonomy in Weapon Systems: DoD Directive 3000.09", "type": "report", "canonicalUrl": "https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodd/300009p.pdf", "authors": [ { "name": "OUSD(P)", "type": "Organization" } ], "publisher": { "name": "esd.whs.mil", "url": "https://www.esd.whs.mil" }, "datePublished": null, "access": "public", "schemaType": "Report", "preview": { "sourcePage": "https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodd/300009p.pdf", "sourceImage": "https://web.archive.org/web/20241230171945id_/https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodd/300009p.pdf", "credit": "OUSD(P)", "kind": "document-page", "alt": "First page of Autonomy in Weapon Systems: DoD Directive 3000.09.", "sourceWidth": 1391, "sourceHeight": 1800, "path": "/img/library/autonomy-in-weapon-systems-dod-directive-3000-09.webp", "width": 1200, "height": 1553, "thumbnail": { "path": "/img/library/autonomy-in-weapon-systems-dod-directive-3000-09-small.webp", "width": 480, "height": 621 }, "pageNumber": 1, "caption": "First page of the 2023 directive, retrieved from the Internet Archive." } }, "summary": "The US Department of Defense directive on autonomy in weapon systems.", "curation": { "note": null, "listingNote": "The US Department of Defense directive on autonomy in weapon systems." }, "topics": [ "Security & defense" ], "tags": [ "Security" ], "related": [ { "id": "please-stop-saying-human-in-the-loop", "reason": "A related argument sharpens the meaning of human responsibility in autonomous systems." }, { "id": "swiss-cheese-model-safeche-process-safety", "reason": "Layered defenses offer a complementary way to think about system safety." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/autonomy-in-weapon-systems-dod-directive-3000-09", "status": "curated", "citedIn": [ { "title": "Industrialiser la GenAI : Build vs Buy", "url": "https://fpaupier.fr/202501_productionize_genai" } ] }, { "id": "please-stop-saying-human-in-the-loop", "slug": "please-stop-saying-human-in-the-loop", "source": { "title": "Please Stop Saying \"Human-In-The-Loop\"", "type": "article", "canonicalUrl": "https://ifc.usafa.edu/articles/please-stop-saying-human-in-the-loop", "authors": [], "publisher": { "name": "ifc.usafa.edu", "url": "https://ifc.usafa.edu" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://ifc.usafa.edu/articles/please-stop-saying-human-in-the-loop", "credit": "ifc.usafa.edu", "kind": "screenshot", "alt": "Screenshot of Please Stop Saying \"Human-In-The-Loop\"", "sourceWidth": 1200, "sourceHeight": 780, "path": "/img/library/please-stop-saying-human-in-the-loop.webp", "width": 1200, "height": 780, "thumbnail": { "path": "/img/library/please-stop-saying-human-in-the-loop-small.webp", "width": 480, "height": 312 }, "capturedAt": "2026-10-13" } }, "summary": "An argument for more precise language about human responsibility in autonomous systems.", "curation": { "note": null, "listingNote": "An argument for more precise language about human responsibility in autonomous systems." }, "topics": [ "Security & defense" ], "tags": [ "Security" ], "related": [ { "id": "autonomy-in-weapon-systems-dod-directive-3000-09", "reason": "The defense directive provides a concrete policy context for autonomy and responsibility." }, { "id": "swiss-cheese-model-safeche-process-safety", "reason": "The Swiss cheese model shifts attention toward multiple layers of defense." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/please-stop-saying-human-in-the-loop", "status": "curated", "citedIn": [ { "title": "Industrialiser la GenAI : Build vs Buy", "url": "https://fpaupier.fr/202501_productionize_genai" } ] }, { "id": "changes-in-openai-api-pricing", "slug": "changes-in-openai-api-pricing", "source": { "title": "Changes in OpenAI API pricing", "type": "social", "canonicalUrl": "https://www.linkedin.com/feed/update/urn:li:activity:7134954498208210944/", "authors": [ { "name": "Peter Gostev", "type": "Person" } ], "publisher": { "name": "LinkedIn", "url": "https://www.linkedin.com" }, "datePublished": null, "access": "public", "schemaType": "SocialMediaPosting", "preview": { "path": "/img/library/ref-bccd8b5dbbda.webp", "width": 954, "height": 743, "sourcePage": "https://www.linkedin.com/feed/update/urn:li:activity:7134954498208210944/", "sourceImage": "https://media.licdn.com/dms/image/v2/D4E22AQHqPfxOvXNd9A/feedshare-image-high-res/feedshare-image-high-res/0/1701105712072?e=2147483647&v=beta&t=fQGVPZQFyqLiNXfaR523bQyyrYGZ-jn7_8GZ5lendBY", "sourceWidth": 954, "sourceHeight": 743, "credit": "LinkedIn", "thumbnail": { "path": "/img/library/ref-bccd8b5dbbda-small.webp", "width": 480, "height": 374 } } }, "summary": "A comparison of changes in OpenAI API pricing.", "curation": { "note": null, "listingNote": "A comparison of changes in OpenAI API pricing." }, "topics": [ "Company building" ], "tags": [ "Economics" ], "related": [ { "id": "llm-api-pricing-calculator", "reason": "Apply pricing information to a model-cost calculation." }, { "id": "llm-price-comparison", "reason": "Compare cost changes alongside model performance." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/changes-in-openai-api-pricing", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "web-operations-keeping-the-data-on-time", "slug": "web-operations-keeping-the-data-on-time", "source": { "title": "Web Operations: Keeping the Data on Time", "type": "book", "canonicalUrl": "https://www.oreilly.com/library/view/web-operations/9781449377465/", "authors": [ { "name": "John Allspaw", "type": "Person" }, { "name": "Jesse Robbins", "type": "Person" } ], "publisher": { "name": "O’Reilly Media", "url": "https://www.oreilly.com" }, "datePublished": null, "access": "purchase", "schemaType": "Book", "publicationYear": 2010, "preview": { "sourcePage": "https://jesserobbins.com/mentions/web-operations-book-allspaw-robbins-oreilly/", "sourceImage": "https://jesserobbins.com/_astro/web-operations-book-cover.d9fx4Vhg_1S2973.webp", "credit": "O’Reilly Media, shared by Jesse Robbins", "kind": "source-image", "caption": "Front and back covers of Web Operations, shared by co-editor Jesse Robbins.", "alt": "Front and back covers of Web Operations: Keeping the Data on Time, with a photograph of schooling barracuda.", "sourceWidth": 1200, "sourceHeight": 769, "path": "/img/library/web-operations-keeping-the-data-on-time.webp", "width": 1200, "height": 769, "thumbnail": { "path": "/img/library/web-operations-keeping-the-data-on-time-small.webp", "width": 480, "height": 308 } } }, "summary": "Operational practices for running web systems, from monitoring and deployment to incident analysis.", "curation": { "note": null, "listingNote": "Operational practices for running web systems, from monitoring and deployment to incident analysis." }, "topics": [ "Engineering" ], "tags": [ "Observability" ], "related": [ { "id": "prometheus-monitoring-system-time-series-database", "reason": "Prometheus provides a concrete monitoring system for operational work." }, { "id": "grafana-k6", "reason": "Load testing complements the book’s deployment and reliability practices." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/web-operations-keeping-the-data-on-time", "status": "curated" }, { "id": "eclairage-sur-n6-chatgpt-ou-la-percee-des-modeles-dia-conversationnels-peren", "slug": "eclairage-sur-n6-chatgpt-ou-la-percee-des-modeles-dia-conversationnels-peren", "source": { "title": "Éclairage sur…n°6 – ChatGPT ou la percée des modèles d’IA conversationnels | PEReN", "type": "article", "canonicalUrl": "https://www.peren.gouv.fr/fr/actualites/2023-04-06_eclairage_sur_chatgpt/", "authors": [], "publisher": { "name": "peren.gouv.fr", "url": "https://www.peren.gouv.fr" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://www.peren.gouv.fr/fr/actualites/2023-04-06_eclairage_sur_chatgpt/", "credit": "peren.gouv.fr", "kind": "screenshot", "alt": "Screenshot of Éclairage sur…n°6 – ChatGPT ou la percée des modèles d’IA conversationnels | PEReN", "sourceWidth": 1200, "sourceHeight": 780, "path": "/img/library/eclairage-sur-n6-chatgpt-ou-la-percee-des-modeles-dia-conversationnels-peren.webp", "width": 1200, "height": 780, "thumbnail": { "path": "/img/library/eclairage-sur-n6-chatgpt-ou-la-percee-des-modeles-dia-conversationnels-peren-small.webp", "width": 480, "height": 312 }, "capturedAt": "2026-10-13" } }, "summary": "A public technical explainer on ChatGPT and conversational AI models.", "curation": { "note": null, "listingNote": "A public technical explainer on ChatGPT and conversational AI models." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "large-language-models-101-history-evolution-and-future", "reason": "A second introduction places conversational models in their historical development." }, { "id": "1hr-talk-intro-to-large-language-models", "reason": "A technical lecture develops the language-model concepts behind the explainer." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/eclairage-sur-n6-chatgpt-ou-la-percee-des-modeles-dia-conversationnels-peren", "status": "curated" }, { "id": "llm-evaluation-doesn-t-need-to-be-complicated", "slug": "llm-evaluation-doesn-t-need-to-be-complicated", "source": { "title": "LLM Evaluation doesn't need to be complicated", "type": "article", "canonicalUrl": "https://www.philschmid.de/llm-evaluation", "authors": [ { "name": "Philipp Schmid", "type": "Person" } ], "publisher": { "name": "philschmid.de", "url": "https://www.philschmid.de" }, "datePublished": "2024-07-11", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-32fa6ab9953d.webp", "width": 1200, "height": 600, "sourcePage": "https://www.philschmid.de/llm-evaluation", "sourceImage": "https://www.philschmid.de/static/blog/llm-evaluation/thumbnail.jpg", "sourceWidth": 1200, "sourceHeight": 600, "credit": "philschmid.de", "thumbnail": { "path": "/img/library/ref-32fa6ab9953d-small.webp", "width": 480, "height": 240 } } }, "summary": "A practical introduction to evaluating language models.", "curation": { "note": null, "listingNote": "A practical introduction to evaluating language models." }, "topics": [ "AI & machine learning" ], "tags": [ "Evaluation" ], "related": [ { "id": "ai-product-evals", "reason": "Move from model evaluation to checks tailored to an AI product." }, { "id": "feedback-functions", "reason": "Compare the kinds of feedback functions available for evaluation." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/llm-evaluation-doesn-t-need-to-be-complicated", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "understanding-and-mitigating-model-drift-in-machine-learning", "slug": "understanding-and-mitigating-model-drift-in-machine-learning", "source": { "title": "Understanding and Mitigating Model Drift in Machine Learning", "type": "article", "canonicalUrl": "https://www.projectpro.io/article/model-drift-in-machine-learning/871", "authors": [], "publisher": { "name": "ProjectPro", "url": "https://www.projectpro.io" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-a1b55132c81c.webp", "width": 1200, "height": 720, "sourcePage": "https://www.projectpro.io/article/model-drift-in-machine-learning/871", "sourceImage": "https://daxg39y63pxwu.cloudfront.net/images/blog/model-drift-in-machine-learning/Understanding_and_Mitigating_Model_Drift_in_Machine_Learning.png", "sourceWidth": 2084, "sourceHeight": 1250, "credit": "ProjectPro", "thumbnail": { "path": "/img/library/ref-a1b55132c81c-small.webp", "width": 480, "height": 288 } } }, "summary": "An overview of detecting and responding to model drift.", "curation": { "note": null, "listingNote": "An overview of detecting and responding to model drift." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "hidden-technical-debt", "reason": "Model drift illustrates the ongoing dependencies and maintenance costs of ML systems." }, { "id": "ai-engineering-flywheel", "reason": "Monitoring and feedback loops support continued improvement after deployment." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/understanding-and-mitigating-model-drift-in-machine-learning", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "safran-buys-ai-firm-preligens-for-220-million-euros", "slug": "safran-buys-ai-firm-preligens-for-220-million-euros", "source": { "title": "Safran buys AI firm Preligens for 220 million euros", "type": "article", "canonicalUrl": "https://www.reuters.com/markets/deals/safran-buys-ai-firm-preligens-220-million-euros-2024-09-02/", "authors": [ { "name": "Michal Aleksandrowicz", "type": "Person" } ], "publisher": { "name": "Reuters", "url": "https://www.reuters.com" }, "datePublished": "2024-09-02", "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://finance.yahoo.com/news/safran-buys-ai-firm-preligens-065604597.html", "sourceImage": "https://media.zenfs.com/en/reuters-finance.com/7c7dca256c5df6650f8e766bfdd3ae01", "credit": "Reuters, via Yahoo Finance", "kind": "source-image", "alt": "Safran equipment at the Eurosatory defence event, from the Reuters article.", "sourceWidth": 800, "sourceHeight": 533, "path": "/img/library/safran-buys-ai-firm-preligens-for-220-million-euros.webp", "width": 800, "height": 533, "thumbnail": { "path": "/img/library/safran-buys-ai-firm-preligens-for-220-million-euros-small.webp", "width": 480, "height": 320 } } }, "summary": "Reporting on Safran’s acquisition of Preligens and its integration as Safran.AI.", "curation": { "note": null, "listingNote": "Reporting on Safran’s acquisition of Preligens and its integration as Safran.AI." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "how-we-built-an-ai-factory-part-1", "reason": "The AI-factory account gives technical and organizational context on Preligens." }, { "id": "intel-announces-launch-of-enterprise-gen-ai-spinoff-articul8", "reason": "Compare another AI business integrated with an established technology company." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/safran-buys-ai-firm-preligens-for-220-million-euros", "status": "curated", "citedIn": [ { "title": "Industrialiser la GenAI : Build vs Buy", "url": "https://fpaupier.fr/202501_productionize_genai" } ] }, { "id": "calculating-gpu-memory-for-serving-llms", "slug": "calculating-gpu-memory-for-serving-llms", "source": { "title": "Calculating GPU memory for serving LLMs", "type": "article", "canonicalUrl": "https://www.substratus.ai/blog/calculating-gpu-memory-for-llm", "authors": [], "publisher": { "name": "Substratus", "url": "https://www.substratus.ai" }, "datePublished": "2023-11-16", "access": "public", "schemaType": "Article", "availability": "unavailable", "preview": { "sourcePage": "https://substratusai.github.io/blog/calculating-gpu-memory-for-llm/", "sourceImage": "https://substratusai.github.io/img/llm-gpu-mem-formula.png", "credit": "Substratus", "kind": "source-image", "alt": "Substratus GPU memory formula and table explaining model parameters, precision and overhead.", "sourceWidth": 978, "sourceHeight": 738, "path": "/img/library/calculating-gpu-memory-for-serving-llms.webp", "width": 978, "height": 738, "thumbnail": { "path": "/img/library/calculating-gpu-memory-for-serving-llms-small.webp", "width": 480, "height": 362 }, "caption": "Original GPU memory formula and reference table" } }, "summary": "A practical estimate of GPU memory needs from model size and numeric precision.", "curation": { "note": null, "listingNote": "A practical estimate of GPU memory needs from model size and numeric precision." }, "topics": [ "Engineering" ], "tags": [ "Inference" ], "related": [ { "id": "transformers-kv-caching-explained", "reason": "KV caching explains an additional source of memory use during generation." }, { "id": "pagedattention", "reason": "PagedAttention addresses how serving systems manage that cache efficiently." } ], "archive": { "snapshotUrl": "https://web.archive.org/web/20250724180635/https://www.substratus.ai/blog/calculating-gpu-memory-for-llm" }, "detailUrl": "https://fpaupier.fr/library/calculating-gpu-memory-for-serving-llms", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "patent-landscape-report-generative-artificial-intelligence", "slug": "patent-landscape-report-generative-artificial-intelligence", "source": { "title": "Patent Landscape Report: Generative Artificial Intelligence", "type": "report", "canonicalUrl": "https://www.wipo.int/web-publications/patent-landscape-report-generative-artificial-intelligence-genai/en/key-findings-and-insights.html", "authors": [], "publisher": { "name": "World Intellectual Property Organization", "url": "https://www.wipo.int/" }, "datePublished": null, "access": "public", "schemaType": "Report", "preview": { "path": "/img/library/ref-83a451356894.webp", "width": 1200, "height": 630, "sourcePage": "https://www.wipo.int/web-publications/patent-landscape-report-generative-artificial-intelligence-genai/en/key-findings-and-insights.html", "sourceImage": "https://www.wipo.int/web-publications/patent-landscape-report-generative-artificial-intelligence-genai/assets/62311/2007_GenAi-feature.jpg", "sourceWidth": 1200, "sourceHeight": 630, "credit": "Patent Landscape Report - Generative Artificial Intelligence (GenAI)", "thumbnail": { "path": "/img/library/ref-83a451356894-small.webp", "width": 480, "height": 252 } } }, "summary": "WIPO’s analysis of the patent landscape around generative AI.", "curation": { "note": null, "listingNote": "WIPO’s analysis of the patent landscape around generative AI." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "european-open-source-ai-index", "reason": "Compare a patent-oriented view of the AI landscape with an openness-oriented index." }, { "id": "europe-big-tech", "reason": "European technology-industry reporting adds context to the competitive landscape." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/patent-landscape-report-generative-artificial-intelligence", "status": "curated", "citedIn": [ { "title": "LLM : souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "carrying-out-error-analysis-c3w2l01", "slug": "carrying-out-error-analysis-c3w2l01", "source": { "title": "Carrying Out Error Analysis (C3W2L01)", "type": "video", "canonicalUrl": "https://www.youtube.com/watch?v=JoAxZsdw_3w", "authors": [], "publisher": { "name": "DeepLearningAI", "url": "https://www.youtube.com" }, "datePublished": null, "access": "public", "schemaType": "VideoObject", "preview": { "id": "carrying-out-error-analysis-c3w2l01", "sourcePage": "https://www.youtube.com/watch?v=JoAxZsdw_3w", "sourceImage": "https://i.ytimg.com/vi/JoAxZsdw_3w/hqdefault.jpg", "credit": "DeepLearningAI / YouTube", "sourceWidth": 480, "sourceHeight": 360, "width": 480, "height": 360, "path": "/img/library/carrying-out-error-analysis-c3w2l01.webp", "thumbnail": { "path": "/img/library/carrying-out-error-analysis-c3w2l01-small.webp", "width": 480, "height": 360 }, "alt": "Video thumbnail for Carrying Out Error Analysis (C3W2L01).", "kind": "video-thumbnail" } }, "summary": "Andrew Ng’s lesson on using error analysis to guide machine-learning work.", "curation": { "note": null, "listingNote": "Andrew Ng’s lesson on using error analysis to guide machine-learning work." }, "topics": [ "Product & design" ], "tags": [ "Product engineering" ], "related": [ { "id": "carrying-out-error-analysis", "reason": "The accompanying slides provide a reference for the lecture’s error-analysis method." }, { "id": "ai-product-evals", "reason": "Apply the same output-inspection approach to AI-product evaluation." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/carrying-out-error-analysis-c3w2l01", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "rex-ia-generative-avec-carrefourfrance-iagenerative", "slug": "rex-ia-generative-avec-carrefourfrance-iagenerative", "source": { "title": "REX IA Générative avec @CarrefourFrance #iagenerative", "type": "video", "canonicalUrl": "https://www.youtube.com/watch?v=Pgz_41nCOeM", "authors": [], "publisher": { "name": "hymaïa", "url": "https://www.youtube.com" }, "datePublished": null, "access": "public", "schemaType": "VideoObject", "preview": { "id": "rex-ia-generative-avec-carrefourfrance-iagenerative", "sourcePage": "https://www.youtube.com/watch?v=Pgz_41nCOeM", "sourceImage": "https://i.ytimg.com/vi/Pgz_41nCOeM/maxresdefault.jpg", "credit": "hymaïa / YouTube", "sourceWidth": 1280, "sourceHeight": 720, "width": 1200, "height": 675, "path": "/img/library/rex-ia-generative-avec-carrefourfrance-iagenerative.webp", "thumbnail": { "path": "/img/library/rex-ia-generative-avec-carrefourfrance-iagenerative-small.webp", "width": 480, "height": 270 }, "alt": "Video thumbnail for REX IA Générative avec @CarrefourFrance #iagenerative.", "kind": "video-thumbnail" } }, "summary": "A discussion of Carrefour’s experience applying generative AI.", "curation": { "note": null, "listingNote": "A discussion of Carrefour’s experience applying generative AI." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "gen-ai-at-work-the-story-of-carrefour-marketing-studio-horizons-by-carrefour", "reason": "The Carrefour Marketing Studio article is a closely related company case study." }, { "id": "qonto-how-qonto-achieved-70-faster-localization-and-streamlined-operations-with-ai-ag", "reason": "Compare Carrefour’s experience with Qonto’s operational use of AI agents." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/rex-ia-generative-avec-carrefourfrance-iagenerative", "status": "curated" }, { "id": "efficient-estimation-of-word-representations-in-vector-space", "slug": "efficient-estimation-of-word-representations-in-vector-space", "source": { "title": "Efficient Estimation of Word Representations in Vector Space", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/1301.3781v3", "authors": [ { "name": "Tomas Mikolov", "type": "Person" }, { "name": "Kai Chen", "type": "Person" }, { "name": "Greg Corrado", "type": "Person" }, { "name": "Jeffrey Dean", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2013-01-16", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/1301.3781v3", "preview": { "path": "/img/library/ref-bfb95f89e35f.webp", "width": 1200, "height": 1552, "sourcePage": "https://arxiv.org/abs/1301.3781v3", "sourceImage": "https://arxiv.org/pdf/1301.3781v3", "sourceWidth": 1237, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Efficient Estimation of Word Representations in Vector Space.", "thumbnail": { "path": "/img/library/ref-bfb95f89e35f-small.webp", "width": 480, "height": 621 } } }, "summary": "The word2vec paper on efficient learning of distributed word representations.", "curation": { "note": null, "listingNote": "The word2vec paper on efficient learning of distributed word representations." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval" ], "related": [ { "id": "text-and-code-embeddings-by-contrastive-pre-training", "reason": "Compare word2vec with contrastive representation learning for text and code." }, { "id": "train-400x-faster-static-embedding-models-with-sentence-transformers", "reason": "Static embedding models revisit efficient representation learning in practical tooling." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/efficient-estimation-of-word-representations-in-vector-space", "status": "curated" }, { "id": "learning-transferable-visual-models-from-natural-language-supervision", "slug": "learning-transferable-visual-models-from-natural-language-supervision", "source": { "title": "Learning Transferable Visual Models From Natural Language Supervision", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2103.00020v1", "authors": [ { "name": "Alec Radford", "type": "Person" }, { "name": "Jong Wook Kim", "type": "Person" }, { "name": "Chris Hallacy", "type": "Person" }, { "name": "Aditya Ramesh", "type": "Person" }, { "name": "Gabriel Goh", "type": "Person" }, { "name": "Sandhini Agarwal", "type": "Person" }, { "name": "Girish Sastry", "type": "Person" }, { "name": "Amanda Askell", "type": "Person" }, { "name": "Pamela Mishkin", "type": "Person" }, { "name": "Jack Clark", "type": "Person" }, { "name": "Gretchen Krueger", "type": "Person" }, { "name": "Ilya Sutskever", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2021-02-26", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2103.00020v1", "preview": { "path": "/img/library/ref-2f4691de4e00.webp", "width": 1200, "height": 1552, "sourcePage": "https://arxiv.org/abs/2103.00020v1", "sourceImage": "https://arxiv.org/pdf/2103.00020v1", "sourceWidth": 1237, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Learning Transferable Visual Models From Natural Language Supervision.", "thumbnail": { "path": "/img/library/ref-2f4691de4e00-small.webp", "width": 480, "height": 621 } } }, "summary": "The CLIP paper on learning visual representations from paired image and text data.", "curation": { "note": null, "listingNote": "The CLIP paper on learning visual representations from paired image and text data." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "colpali-efficient-document-retrieval-with-vision-language-models", "reason": "ColPali applies vision-language representations to document retrieval." }, { "id": "apple-aiml-research-ml-ferret", "reason": "Ferret is another research implementation linking visual content and language." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/learning-transferable-visual-models-from-natural-language-supervision", "status": "curated" }, { "id": "text-and-code-embeddings-by-contrastive-pre-training", "slug": "text-and-code-embeddings-by-contrastive-pre-training", "source": { "title": "Text and Code Embeddings by Contrastive Pre-Training", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2201.10005v1", "authors": [ { "name": "Arvind Neelakantan", "type": "Person" }, { "name": "Tao Xu", "type": "Person" }, { "name": "Raul Puri", "type": "Person" }, { "name": "Alec Radford", "type": "Person" }, { "name": "Jesse Michael Han", "type": "Person" }, { "name": "Jerry Tworek", "type": "Person" }, { "name": "Qiming Yuan", "type": "Person" }, { "name": "Nikolas Tezak", "type": "Person" }, { "name": "Jong Wook Kim", "type": "Person" }, { "name": "Chris Hallacy", "type": "Person" }, { "name": "Johannes Heidecke", "type": "Person" }, { "name": "Pranav Shyam", "type": "Person" }, { "name": "Boris Power", "type": "Person" }, { "name": "Tyna Eloundou Nekoul", "type": "Person" }, { "name": "Girish Sastry", "type": "Person" }, { "name": "Gretchen Krueger", "type": "Person" }, { "name": "David Schnurr", "type": "Person" }, { "name": "Felipe Petroski Such", "type": "Person" }, { "name": "Kenny Hsu", "type": "Person" }, { "name": "Madeleine Thompson", "type": "Person" }, { "name": "Tabarak Khan", "type": "Person" }, { "name": "Toki Sherbakov", "type": "Person" }, { "name": "Joanne Jang", "type": "Person" }, { "name": "Peter Welinder", "type": "Person" }, { "name": "Lilian Weng", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2022-01-24", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2201.10005v1", "preview": { "path": "/img/library/ref-2cffb0e12938.webp", "width": 1200, "height": 1552, "sourcePage": "https://arxiv.org/abs/2201.10005v1", "sourceImage": "https://arxiv.org/pdf/2201.10005v1", "sourceWidth": 1237, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Text and Code Embeddings by Contrastive Pre-Training.", "thumbnail": { "path": "/img/library/ref-2cffb0e12938-small.webp", "width": 480, "height": 621 } } }, "summary": "Research on learning representations for text and code using contrastive training.", "curation": { "note": null, "listingNote": "Research on learning representations for text and code using contrastive training." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval" ], "related": [ { "id": "efficient-estimation-of-word-representations-in-vector-space", "reason": "Word2vec provides an earlier approach to learning distributed language representations." }, { "id": "openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddings", "reason": "A critical analysis examines the evaluation of OpenAI text embeddings." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/text-and-code-embeddings-by-contrastive-pre-training", "status": "curated" }, { "id": "chatgpt-as-a-factual-inconsistency-evaluator-for-text-summarization", "slug": "chatgpt-as-a-factual-inconsistency-evaluator-for-text-summarization", "source": { "title": "ChatGPT as a Factual Inconsistency Evaluator for Text Summarization", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2303.15621v2", "authors": [ { "name": "Zheheng Luo", "type": "Person" }, { "name": "Qianqian Xie", "type": "Person" }, { "name": "Sophia Ananiadou", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2023-03-27", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2303.15621v2", "preview": { "path": "/img/library/ref-8f39d872ae80.webp", "width": 1132, "height": 1600, "sourcePage": "https://arxiv.org/abs/2303.15621v2", "sourceImage": "https://arxiv.org/pdf/2303.15621v2", "sourceWidth": 1132, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of ChatGPT as a Factual Inconsistency Evaluator for Text Summarization.", "thumbnail": { "path": "/img/library/ref-8f39d872ae80-small.webp", "width": 453, "height": 640 } } }, "summary": "An evaluation of ChatGPT for detecting factual inconsistencies in summaries.", "curation": { "note": null, "listingNote": "An evaluation of ChatGPT for detecting factual inconsistencies in summaries." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "human-like-summarization-evaluation-with-chatgpt", "reason": "Compare another use of ChatGPT for judging generated summaries." }, { "id": "acueval-fine-grained-hallucination-evaluation-and-correction-for-abstractive-summariz", "reason": "ACUEval makes hallucination evaluation more granular by decomposing summaries into claims." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/chatgpt-as-a-factual-inconsistency-evaluator-for-text-summarization", "status": "curated" }, { "id": "gpt4graph-can-large-language-models-understand-graph-structured-data-an-empirical-eva", "slug": "gpt4graph-can-large-language-models-understand-graph-structured-data-an-empirical-eva", "source": { "title": "GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2305.15066v2", "authors": [ { "name": "Jiayan Guo", "type": "Person" }, { "name": "Lun Du", "type": "Person" }, { "name": "Hengyu Liu", "type": "Person" }, { "name": "Mengyu Zhou", "type": "Person" }, { "name": "Xinyi He", "type": "Person" }, { "name": "Shi Han", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2023-05-24", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2305.15066v2", "preview": { "path": "/img/library/ref-84c541d2f966.webp", "width": 1132, "height": 1600, "sourcePage": "https://arxiv.org/abs/2305.15066v2", "sourceImage": "https://arxiv.org/pdf/2305.15066v2", "sourceWidth": 1132, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking.", "thumbnail": { "path": "/img/library/ref-84c541d2f966-small.webp", "width": 453, "height": 640 } } }, "summary": "An empirical study of how language models handle graph-structured information.", "curation": { "note": null, "listingNote": "An empirical study of how language models handle graph-structured information." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Evaluation", "Knowledge graphs" ], "related": [ { "id": "end-to-end-graph-flattening-method-for-large-language-models", "reason": "Graph flattening addresses how structured graphs can be represented for language models." }, { "id": "injecting-knowledge-graphs-into-large-language-models", "reason": "Knowledge-graph injection explores another route to using structured information." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/gpt4graph-can-large-language-models-understand-graph-structured-data-an-empirical-eva", "status": "curated" }, { "id": "evaluating-correctness-and-faithfulness-of-instruction-following-models-for-question", "slug": "evaluating-correctness-and-faithfulness-of-instruction-following-models-for-question", "source": { "title": "Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2307.16877v2", "authors": [ { "name": "Vaibhav Adlakha", "type": "Person" }, { "name": "Parishad BehnamGhader", "type": "Person" }, { "name": "Xing Han Lu", "type": "Person" }, { "name": "Nicholas Meade", "type": "Person" }, { "name": "Siva Reddy", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2023-07-31", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2307.16877v2", "preview": { "path": "/img/library/ref-4a7ce10d48bd.webp", "width": 1132, "height": 1600, "sourcePage": "https://arxiv.org/abs/2307.16877v2", "sourceImage": "https://arxiv.org/pdf/2307.16877v2", "sourceWidth": 1132, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering.", "thumbnail": { "path": "/img/library/ref-4a7ce10d48bd-small.webp", "width": 453, "height": 640 } } }, "summary": "Methods for assessing correctness and faithfulness in question answering.", "curation": { "note": null, "listingNote": "Methods for assessing correctness and faithfulness in question answering." }, "topics": [ "AI & machine learning" ], "tags": [ "Evaluation" ], "related": [ { "id": "the-rag-triad", "reason": "The RAG triad separates related questions of grounding and answer relevance." }, { "id": "llm-as-judge", "reason": "LLM-judge research examines the reliability of automated evaluation itself." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/evaluating-correctness-and-faithfulness-of-instruction-following-models-for-question", "status": "curated" }, { "id": "large-language-models-for-information-retrieval-a-survey", "slug": "large-language-models-for-information-retrieval-a-survey", "source": { "title": "Large Language Models for Information Retrieval: A Survey", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2308.07107v2", "authors": [ { "name": "Yutao Zhu", "type": "Person" }, { "name": "Huaying Yuan", "type": "Person" }, { "name": "Shuting Wang", "type": "Person" }, { "name": "Jiongnan Liu", "type": "Person" }, { "name": "Wenhan Liu", "type": "Person" }, { "name": "Chenlong Deng", "type": "Person" }, { "name": "Zhicheng Dou", "type": "Person" }, { "name": "Ji-Rong Wen", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2023-08-14", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2308.07107v2", "preview": { "path": "/img/library/ref-3bfa20dfbda0.webp", "width": 1200, "height": 1552, "sourcePage": "https://arxiv.org/abs/2308.07107v2", "sourceImage": "https://arxiv.org/pdf/2308.07107v2", "sourceWidth": 1237, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Large Language Models for Information Retrieval: A Survey.", "thumbnail": { "path": "/img/library/ref-3bfa20dfbda0-small.webp", "width": 480, "height": 621 } } }, "summary": "A survey of language-model applications in information retrieval.", "curation": { "note": null, "listingNote": "A survey of language-model applications in information retrieval." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "introduction-information-retrieval", "reason": "The textbook supplies the classical retrieval foundations behind the survey." }, { "id": "air-bench-automated-heterogeneous-information-retrieval-benchmark", "reason": "AIR-Bench provides a concrete benchmark spanning retrieval tasks and domains." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/large-language-models-for-information-retrieval-a-survey", "status": "curated" }, { "id": "ragas-automated-evaluation-of-retrieval-augmented-generation", "slug": "ragas-automated-evaluation-of-retrieval-augmented-generation", "source": { "title": "RAGAS: Automated Evaluation of Retrieval Augmented Generation", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2309.15217v1", "authors": [ { "name": "Shahul Es", "type": "Person" }, { "name": "Jithin James", "type": "Person" }, { "name": "Luis Espinosa-Anke", "type": "Person" }, { "name": "Steven Schockaert", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2023-09-26", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2309.15217v1", "preview": { "path": "/img/library/ref-3c3f713855c1.webp", "width": 1132, "height": 1600, "sourcePage": "https://arxiv.org/abs/2309.15217v1", "sourceImage": "https://arxiv.org/pdf/2309.15217v1", "sourceWidth": 1132, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Ragas: Automated Evaluation of Retrieval Augmented Generation.", "thumbnail": { "path": "/img/library/ref-3c3f713855c1-small.webp", "width": 453, "height": 640 } } }, "summary": "The Ragas paper on evaluating retrieval-augmented generation systems.", "curation": { "note": null, "listingNote": "The Ragas paper on evaluating retrieval-augmented generation systems." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "metrics-ragas", "reason": "The documentation describes the metrics implemented by the Ragas framework." }, { "id": "the-rag-triad", "reason": "Compare Ragas with the groundedness, context-relevance, and answer-relevance triad." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/ragas-automated-evaluation-of-retrieval-augmented-generation", "status": "curated" }, { "id": "flattenquant-breaking-through-the-inference-compute-bound-for-large-language-models-w", "slug": "flattenquant-breaking-through-the-inference-compute-bound-for-large-language-models-w", "source": { "title": "FlattenQuant: Breaking Through the Inference Compute-bound for Large Language Models with Per-tensor Quantization", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2402.17985v1", "authors": [ { "name": "Yi Zhang", "type": "Person" }, { "name": "Fei Yang", "type": "Person" }, { "name": "Shuang Peng", "type": "Person" }, { "name": "Fangyu Wang", "type": "Person" }, { "name": "Aimin Pan", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2024-02-28", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2402.17985v1", "preview": { "path": "/img/library/ref-85cd553af7ff.webp", "width": 1132, "height": 1600, "sourcePage": "https://arxiv.org/abs/2402.17985v1", "sourceImage": "https://arxiv.org/pdf/2402.17985v1", "sourceWidth": 1132, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of FlattenQuant: Breaking Through the Inference Compute-bound for Large Language Models with Per-tensor Quantization.", "thumbnail": { "path": "/img/library/ref-85cd553af7ff-small.webp", "width": 453, "height": 640 } } }, "summary": "A quantization method designed to improve language-model inference efficiency.", "curation": { "note": null, "listingNote": "A quantization method designed to improve language-model inference efficiency." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Model adaptation", "Inference", "Product engineering" ], "related": [ { "id": "bitsandbytes-foundation-bitsandbytes", "reason": "Low-bit model tooling provides implementation context for inference quantization." }, { "id": "tinyml-talks-a-practical-guide-to-neural-network-quantization", "reason": "A practical talk develops the general principles of neural-network quantization." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/flattenquant-breaking-through-the-inference-compute-bound-for-large-language-models-w", "status": "curated" }, { "id": "soar-improved-indexing-for-approximate-nearest-neighbor-search", "slug": "soar-improved-indexing-for-approximate-nearest-neighbor-search", "source": { "title": "SOAR: Improved Indexing for Approximate Nearest Neighbor Search", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2404.00774v1", "authors": [ { "name": "Philip Sun", "type": "Person" }, { "name": "David Simcha", "type": "Person" }, { "name": "Dave Dopson", "type": "Person" }, { "name": "Ruiqi Guo", "type": "Person" }, { "name": "Sanjiv Kumar", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2024-03-31", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2404.00774v1", "preview": { "path": "/img/library/ref-2a05d81cfbd9.webp", "width": 1200, "height": 1552, "sourcePage": "https://arxiv.org/abs/2404.00774v1", "sourceImage": "https://arxiv.org/pdf/2404.00774v1", "sourceWidth": 1237, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of SOAR: Improved Indexing for Approximate Nearest Neighbor Search.", "thumbnail": { "path": "/img/library/ref-2a05d81cfbd9-small.webp", "width": 480, "height": 621 } } }, "summary": "An indexing approach for approximate nearest-neighbor search.", "curation": { "note": null, "listingNote": "An indexing approach for approximate nearest-neighbor search." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "efficient-and-robust-approximate-nearest-neighbor-search-using-hierarchical-navigable", "reason": "Compare this indexing method with graph-based approximate nearest-neighbor search." }, { "id": "evaluation-of-approximate-nearest-neighbors-large-datasets", "reason": "Public vector datasets support evaluation of these indexing methods." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/soar-improved-indexing-for-approximate-nearest-neighbor-search", "status": "curated" }, { "id": "from-local-to-global-a-graph-rag-approach-to-query-focused-summarization", "slug": "from-local-to-global-a-graph-rag-approach-to-query-focused-summarization", "source": { "title": "From Local to Global: A Graph RAG Approach to Query-Focused Summarization", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2404.16130v1", "authors": [ { "name": "Darren Edge", "type": "Person" }, { "name": "Ha Trinh", "type": "Person" }, { "name": "Newman Cheng", "type": "Person" }, { "name": "Joshua Bradley", "type": "Person" }, { "name": "Alex Chao", "type": "Person" }, { "name": "Apurva Mody", "type": "Person" }, { "name": "Steven Truitt", "type": "Person" }, { "name": "Jonathan Larson", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2024-04-24", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2404.16130v1", "preview": { "path": "/img/library/ref-a8da547bf3cd.webp", "width": 1200, "height": 1552, "sourcePage": "https://arxiv.org/abs/2404.16130v1", "sourceImage": "https://arxiv.org/pdf/2404.16130v1", "sourceWidth": 1237, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of From Local to Global: A Graph RAG Approach to Query-Focused Summarization.", "thumbnail": { "path": "/img/library/ref-a8da547bf3cd-small.webp", "width": 480, "height": 621 } } }, "summary": "The GraphRAG paper on graph-based retrieval for broad, query-focused summaries.", "curation": { "note": null, "listingNote": "The GraphRAG paper on graph-based retrieval for broad, query-focused summaries." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Retrieval", "Evaluation", "Knowledge graphs" ], "related": [ { "id": "rag-vs-graphrag", "reason": "A systematic comparison tests where GraphRAG helps relative to conventional RAG." }, { "id": "leiden-clustering-for-community-detection-a-step-by-step-guide-with-python-implementa", "reason": "Community detection is a related graph-analysis technique to explore." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/from-local-to-global-a-graph-rag-approach-to-query-focused-summarization", "status": "curated" }, { "id": "recent-advances-in-text-embedding-a-comprehensive-review-of-top-performing-methods-on", "slug": "recent-advances-in-text-embedding-a-comprehensive-review-of-top-performing-methods-on", "source": { "title": "Recent advances in text embedding: A Comprehensive Review of Top-Performing Methods on the MTEB Benchmark", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2406.01607v2", "authors": [ { "name": "Hongliu Cao", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2024-05-27", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2406.01607v2", "preview": { "path": "/img/library/ref-533d02aebf92.webp", "width": 1200, "height": 1552, "sourcePage": "https://arxiv.org/abs/2406.01607v2", "sourceImage": "https://arxiv.org/pdf/2406.01607v2", "sourceWidth": 1237, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Recent advances in text embedding: A Comprehensive Review of Top-Performing Methods on the MTEB Benchmark.", "thumbnail": { "path": "/img/library/ref-533d02aebf92-small.webp", "width": 480, "height": 621 } } }, "summary": "A review of text-embedding methods evaluated on MTEB.", "curation": { "note": null, "listingNote": "A review of text-embedding methods evaluated on MTEB." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "mteb-leaderboard-a-hugging-face-space-by-mteb", "reason": "Inspect the benchmark leaderboard discussed in the review." }, { "id": "openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddings", "reason": "A focused critique examines how embedding quality is measured." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/recent-advances-in-text-embedding-a-comprehensive-review-of-top-performing-methods-on", "status": "curated" }, { "id": "finesure-fine-grained-summarization-evaluation-using-llms", "slug": "finesure-fine-grained-summarization-evaluation-using-llms", "source": { "title": "FineSurE: Fine-grained Summarization Evaluation using LLMs", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2407.00908v3", "authors": [ { "name": "Hwanjun Song", "type": "Person" }, { "name": "Hang Su", "type": "Person" }, { "name": "Igor Shalyminov", "type": "Person" }, { "name": "Jason Cai", "type": "Person" }, { "name": "Saab Mansour", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2024-07-01", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2407.00908v3", "preview": { "path": "/img/library/ref-ab732871f4c1.webp", "width": 1132, "height": 1600, "sourcePage": "https://arxiv.org/abs/2407.00908v3", "sourceImage": "https://arxiv.org/pdf/2407.00908v3", "sourceWidth": 1132, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of FineSurE: Fine-grained Summarization Evaluation using LLMs.", "thumbnail": { "path": "/img/library/ref-ab732871f4c1-small.webp", "width": 453, "height": 640 } } }, "summary": "A fine-grained approach to evaluating summaries with language models.", "curation": { "note": null, "listingNote": "A fine-grained approach to evaluating summaries with language models." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "evaluate-summarization-in-fine-granularity-auto-evaluation-with-llm", "reason": "Compare another study of fine-grained summarization evaluation." }, { "id": "acueval-fine-grained-hallucination-evaluation-and-correction-for-abstractive-summariz", "reason": "Claim-level hallucination checks offer a complementary evaluation dimension." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/finesure-fine-grained-summarization-evaluation-using-llms", "status": "curated" }, { "id": "colpali-efficient-document-retrieval-with-vision-language-models", "slug": "colpali-efficient-document-retrieval-with-vision-language-models", "source": { "title": "ColPali: Efficient Document Retrieval with Vision Language Models", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2407.01449v3", "authors": [ { "name": "Manuel Faysse", "type": "Person" }, { "name": "Hugues Sibille", "type": "Person" }, { "name": "Tony Wu", "type": "Person" }, { "name": "Bilel Omrani", "type": "Person" }, { "name": "Gautier Viaud", "type": "Person" }, { "name": "Céline Hudelot", "type": "Person" }, { "name": "Pierre Colombo", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2024-06-27", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2407.01449v3", "preview": { "path": "/img/library/ref-2f87b0ea2fa1.webp", "width": 1132, "height": 1600, "sourcePage": "https://arxiv.org/abs/2407.01449v3", "sourceImage": "https://arxiv.org/pdf/2407.01449v3", "sourceWidth": 1132, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of ColPali: Efficient Document Retrieval with Vision Language Models.", "thumbnail": { "path": "/img/library/ref-2f87b0ea2fa1-small.webp", "width": 453, "height": 640 } } }, "summary": "A retrieval method that uses vision-language representations of document pages.", "curation": { "note": null, "listingNote": "A retrieval method that uses vision-language representations of document pages." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "learning-transferable-visual-models-from-natural-language-supervision", "reason": "CLIP provides background on learning from paired image and text information." }, { "id": "recent-advances-in-text-embedding-a-comprehensive-review-of-top-performing-methods-on", "reason": "Compare vision-language document retrieval with text-embedding retrieval methods." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/colpali-efficient-document-retrieval-with-vision-language-models", "status": "curated" }, { "id": "systematic-evaluation-of-llm-as-a-judge-in-llm-alignment-tasks-explainable-metrics-an", "slug": "systematic-evaluation-of-llm-as-a-judge-in-llm-alignment-tasks-explainable-metrics-an", "source": { "title": "Systematic Evaluation of LLM-as-a-Judge in LLM Alignment Tasks: Explainable Metrics and Diverse Prompt Templates", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2408.13006v2", "authors": [ { "name": "Hui Wei", "type": "Person" }, { "name": "Shenghua He", "type": "Person" }, { "name": "Tian Xia", "type": "Person" }, { "name": "Fei Liu", "type": "Person" }, { "name": "Andy Wong", "type": "Person" }, { "name": "Jingyang Lin", "type": "Person" }, { "name": "Mei Han", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2024-08-23", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2408.13006v2", "preview": { "path": "/img/library/ref-5e2d6ee66423.webp", "width": 1200, "height": 1552, "sourcePage": "https://arxiv.org/abs/2408.13006v2", "sourceImage": "https://arxiv.org/pdf/2408.13006v2", "sourceWidth": 1237, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Systematic Evaluation of LLM-as-a-Judge in LLM Alignment Tasks: Explainable Metrics and Diverse Prompt Templates.", "thumbnail": { "path": "/img/library/ref-5e2d6ee66423-small.webp", "width": 480, "height": 621 } } }, "summary": "A systematic study of language models used as judges in alignment evaluation.", "curation": { "note": null, "listingNote": "A systematic study of language models used as judges in alignment evaluation." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Evaluation", "Observability" ], "related": [ { "id": "llm-as-judge", "reason": "The MT-Bench and Chatbot Arena study provides another assessment of LLM judges." }, { "id": "style-outweighs-substance-failure-modes-of-llm-judges-in-alignment-benchmarking", "reason": "Style-related failure modes challenge the reliability of those judgments." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/systematic-evaluation-of-llm-as-a-judge-in-llm-alignment-tasks-explainable-metrics-an", "status": "curated" }, { "id": "air-bench-automated-heterogeneous-information-retrieval-benchmark", "slug": "air-bench-automated-heterogeneous-information-retrieval-benchmark", "source": { "title": "AIR-Bench: Automated Heterogeneous Information Retrieval Benchmark", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2412.13102v3", "authors": [ { "name": "Jianlyu Chen", "type": "Person" }, { "name": "Nan Wang", "type": "Person" }, { "name": "Chaofan Li", "type": "Person" }, { "name": "Bo Wang", "type": "Person" }, { "name": "Shitao Xiao", "type": "Person" }, { "name": "Han Xiao", "type": "Person" }, { "name": "Hao Liao", "type": "Person" }, { "name": "Defu Lian", "type": "Person" }, { "name": "Zheng Liu", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2024-12-17", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2412.13102v3", "preview": { "path": "/img/library/ref-fee30566e3ef.webp", "width": 1132, "height": 1600, "sourcePage": "https://arxiv.org/abs/2412.13102v3", "sourceImage": "https://arxiv.org/pdf/2412.13102v3", "sourceWidth": 1132, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of AIR-Bench: Automated Heterogeneous Information Retrieval Benchmark.", "thumbnail": { "path": "/img/library/ref-fee30566e3ef-small.webp", "width": 453, "height": 640 } } }, "summary": "A benchmark for evaluating retrieval across heterogeneous tasks and domains.", "curation": { "note": null, "listingNote": "A benchmark for evaluating retrieval across heterogeneous tasks and domains." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "large-language-models-for-information-retrieval-a-survey", "reason": "The retrieval survey gives context for the tasks covered by AIR-Bench." }, { "id": "mteb-leaderboard-a-hugging-face-space-by-mteb", "reason": "Compare a second benchmark resource for retrieval-related representations." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/air-bench-automated-heterogeneous-information-retrieval-benchmark", "status": "curated" }, { "id": "evaluate-summarization-in-fine-granularity-auto-evaluation-with-llm", "slug": "evaluate-summarization-in-fine-granularity-auto-evaluation-with-llm", "source": { "title": "Evaluate Summarization in Fine-Granularity: Auto Evaluation with LLM", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2412.19906v1", "authors": [ { "name": "Dong Yuan", "type": "Person" }, { "name": "Eti Rastogi", "type": "Person" }, { "name": "Fen Zhao", "type": "Person" }, { "name": "Sagar Goyal", "type": "Person" }, { "name": "Gautam Naik", "type": "Person" }, { "name": "Sree Prasanna Rajagopal", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2024-12-27", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2412.19906v1", "preview": { "path": "/img/library/ref-5cdff908e493.webp", "width": 1132, "height": 1600, "sourcePage": "https://arxiv.org/abs/2412.19906v1", "sourceImage": "https://arxiv.org/pdf/2412.19906v1", "sourceWidth": 1132, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Evaluate Summarization in Fine-Granularity: Auto Evaluation with LLM.", "thumbnail": { "path": "/img/library/ref-5cdff908e493-small.webp", "width": 453, "height": 640 } } }, "summary": "A study of detailed, language-model-based summarization evaluation.", "curation": { "note": null, "listingNote": "A study of detailed, language-model-based summarization evaluation." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "finesure-fine-grained-summarization-evaluation-using-llms", "reason": "FineSurE is another approach to detailed summarization evaluation." }, { "id": "human-like-summarization-evaluation-with-chatgpt", "reason": "Compare fine-grained scoring with judgments intended to resemble human evaluation." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/evaluate-summarization-in-fine-granularity-auto-evaluation-with-llm", "status": "curated" }, { "id": "surveyx-academic-survey-automation-via-large-language-models", "slug": "surveyx-academic-survey-automation-via-large-language-models", "source": { "title": "SurveyX: Academic Survey Automation via Large Language Models", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2502.14776v1", "authors": [ { "name": "Xun Liang", "type": "Person" }, { "name": "Jiawei Yang", "type": "Person" }, { "name": "Yezhaohui Wang", "type": "Person" }, { "name": "Chen Tang", "type": "Person" }, { "name": "Zifan Zheng", "type": "Person" }, { "name": "Simin Niu", "type": "Person" }, { "name": "Shichao Song", "type": "Person" }, { "name": "Hanyu Wang", "type": "Person" }, { "name": "Bo Tang", "type": "Person" }, { "name": "Feiyu Xiong", "type": "Person" }, { "name": "Keming Mao", "type": "Person" }, { "name": "Zhiyu li", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2025-02-20", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2502.14776v1", "preview": { "path": "/img/library/ref-ed5b2619ec75.webp", "width": 1200, "height": 1552, "sourcePage": "https://arxiv.org/abs/2502.14776v1", "sourceImage": "https://arxiv.org/pdf/2502.14776v1", "sourceWidth": 1237, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of SurveyX: Academic Survey Automation via Large Language Models.", "thumbnail": { "path": "/img/library/ref-ed5b2619ec75-small.webp", "width": 480, "height": 621 } } }, "summary": "A system for automating the preparation and writing of academic surveys.", "curation": { "note": null, "listingNote": "A system for automating the preparation and writing of academic surveys." }, "topics": [ "AI & machine learning" ], "tags": [ "Agents" ], "related": [ { "id": "surveyx-and-automated-academic-surveys", "reason": "The companion discussion explains how SurveyX organizes references and surveys." }, { "id": "deepsearch-deepresearch", "reason": "Deep-research workflows offer another approach to iterative information gathering and synthesis." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/surveyx-academic-survey-automation-via-large-language-models", "status": "curated" }, { "id": "refeed-multi-dimensional-summarization-refinement-with-reflective-reasoning-on-feedba", "slug": "refeed-multi-dimensional-summarization-refinement-with-reflective-reasoning-on-feedba", "source": { "title": "ReFeed: Multi-dimensional Summarization Refinement with Reflective Reasoning on Feedback", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2503.21332v1", "authors": [ { "name": "Taewon Yun", "type": "Person" }, { "name": "Jihwan Oh", "type": "Person" }, { "name": "Hyangsuk Min", "type": "Person" }, { "name": "Yuho Lee", "type": "Person" }, { "name": "Jihwan Bang", "type": "Person" }, { "name": "Jason Cai", "type": "Person" }, { "name": "Hwanjun Song", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2025-03-27", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2503.21332v1", "preview": { "path": "/img/library/ref-b4a622790352.webp", "width": 1200, "height": 1552, "sourcePage": "https://arxiv.org/abs/2503.21332v1", "sourceImage": "https://arxiv.org/pdf/2503.21332v1", 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"snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/refeed-multi-dimensional-summarization-refinement-with-reflective-reasoning-on-feedba", "status": "curated" }, { "id": "evaluation-of-approximate-nearest-neighbors-large-datasets", "slug": "evaluation-of-approximate-nearest-neighbors-large-datasets", "source": { "title": "Evaluation of Approximate nearest neighbors: large datasets", "type": "website", "canonicalUrl": "http://corpus-texmex.irisa.fr/", "authors": [], "publisher": { "name": "corpus-texmex.irisa.fr", "url": "http://corpus-texmex.irisa.fr" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "sourcePage": "http://corpus-texmex.irisa.fr/", "credit": "corpus-texmex.irisa.fr", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "TEXMEX benchmark page listing large vector datasets for approximate nearest-neighbor search.", "sourceWidth": 1200, "sourceHeight": 850, "path": 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"width": 1200, "height": 630, "sourcePage": "https://langfuse.com/", "sourceImage": "https://langfuse.com/api/og?title=Langfuse+%E2%80%93+Open+Source+Agent+Evals+%26+Observability&description=Trace%2C+evaluate%2C+and+improve+AI+agents+with+one+open+platform.+Use+production+data+to+understand+behavior%2C+collaborate+on+fixes%2C+and+ship+better+quality+at+lower+cost+and+latency.", "sourceWidth": 1200, "sourceHeight": 630, "credit": "langfuse.com", "thumbnail": { "path": "/img/library/ref-7cc9323a38fb-small.webp", "width": 480, "height": 252 } } }, "summary": "An open platform for tracing and evaluating language-model applications.", "curation": { "note": null, "listingNote": "An open platform for tracing and evaluating language-model applications." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Evaluation", "Observability", "Open source", "Agents" ], "related": [ { "id": "langfuse-langfuse", "reason": "The source repository accompanies this tracing and evaluation platform." }, { "id": "observability-data-model-langfuse", "reason": "The data-model documentation explains how its observations and traces are represented." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/langfuse-open-source-agent-evals-observability", "status": "curated" }, { "id": "finding-the-right-llm-for-your-business-a-practical-cost-benefit-analysis", "slug": "finding-the-right-llm-for-your-business-a-practical-cost-benefit-analysis", "source": { "title": "Finding the Right LLM for Your Business: A Practical Cost-Benefit Analysis", "type": "article", "canonicalUrl": "https://10pearls.com/blog/find-the-right-llm-for-your-business/", "authors": [ { "name": "10Pearls Editorial Team", "type": "Organization" } ], "publisher": { "name": "10Pearls", "url": "https://10pearls.com" }, "datePublished": "2024-04-16", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-3fb67f3ff84b.webp", "width": 640, "height": 359, "sourcePage": "https://10pearls.com/blog/find-the-right-llm-for-your-business/", "sourceImage": "https://10pearls.com/wp-content/uploads/2024/04/blog1-social-update.png", "sourceWidth": 640, "sourceHeight": 359, "credit": "10Pearls", "thumbnail": { "path": "/img/library/ref-3fb67f3ff84b-small.webp", "width": 480, "height": 269 } } }, "summary": "A cost-benefit framework for selecting a language model for a business.", "curation": { "note": null, "listingNote": "A cost-benefit framework for selecting a language model for a business." }, "topics": [ "Company building" ], "tags": [ "Economics" ], "related": [ { "id": "llm-price-comparison", "reason": "A price-performance comparison supplies one input to a model-selection decision." }, { "id": "llm-economics-which-is-cheaper-to-deploy-chatgpt-vs-open-source-llms", "reason": "Compare hosted commercial models with the costs of self-hosted open models." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/finding-the-right-llm-for-your-business-a-practical-cost-benefit-analysis", "status": "curated" }, { "id": "acueval-fine-grained-hallucination-evaluation-and-correction-for-abstractive-summariz", "slug": "acueval-fine-grained-hallucination-evaluation-and-correction-for-abstractive-summariz", "source": { "title": "ACUEval: Fine-grained Hallucination Evaluation and Correction for Abstractive Summarization", "type": "paper", "canonicalUrl": "https://aclanthology.org/2024.findings-acl.597/", "authors": [ { "name": "David Wan", "type": "Person" }, { "name": "Koustuv Sinha", "type": "Person" }, { "name": "Srini Iyer", "type": "Person" }, { "name": "Asli Celikyilmaz", "type": "Person" }, { "name": "Mohit Bansal", "type": "Person" }, { "name": "Ramakanth Pasunuru", "type": "Person" } ], "publisher": { "name": "ACL Anthology", "url": "https://aclanthology.org" }, "datePublished": null, "access": "public", "schemaType": "ScholarlyArticle", "preview": { "sourcePage": 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"Engineering" ], "tags": [ "Retrieval", "Evaluation", "Observability" ], "related": [ { "id": "finesure-fine-grained-summarization-evaluation-using-llms", "reason": "Compare another method that evaluates summaries at a finer level than a single score." }, { "id": "refeed-multi-dimensional-summarization-refinement-with-reflective-reasoning-on-feedba", "reason": "Feedback-driven refinement explores how evaluation results can improve a summary." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/acueval-fine-grained-hallucination-evaluation-and-correction-for-abstractive-summariz", "status": "curated" }, { "id": "rouge-a-package-for-automatic-evaluation-of-summaries", "slug": "rouge-a-package-for-automatic-evaluation-of-summaries", "source": { "title": "ROUGE: A Package for Automatic Evaluation of Summaries", "type": "paper", "canonicalUrl": "https://aclanthology.org/W04-1013/", "authors": [ { "name": "Chin-Yew Lin", "type": "Person" } ], "publisher": { 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"finesure-fine-grained-summarization-evaluation-using-llms", "reason": "FineSurE provides a more granular evaluation approach." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/rouge-a-package-for-automatic-evaluation-of-summaries", "status": "curated" }, { "id": "v-jepa-the-next-step-toward-advanced-machine-intelligence", "slug": "v-jepa-the-next-step-toward-advanced-machine-intelligence", "source": { "title": "V-JEPA: The next step toward advanced machine intelligence", "type": "article", "canonicalUrl": "https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/", "authors": [], "publisher": { "name": "Meta AI", "url": "https://ai.meta.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-f552d964d644.webp", "width": 1200, "height": 675, "sourcePage": "https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/", "sourceImage": "https://scontent-cdg6-1.xx.fbcdn.net/v/t39.2365-6/427903438_940629940810199_2360448818907290525_n.jpg?_nc_cat=103&ccb=1-7&_nc_sid=e280be&_nc_ohc=7PQ_cp3ZKU4Q7kNvwHV-rvl&_nc_oc=AdoPJdbklPiF7xlmv-vFFz6x8c2jxvCeN_D8SjuGoWCVptDC28i-P-ppwXqtOkqzYqiMZIWG8kFOaneRpop1q9Se&_nc_zt=14&_nc_ht=scontent-cdg6-1.xx&_nc_gid=tNMg1xRfVAyP2-tn9W_Ppw&_nc_ss=7b20f&oh=00_AQOF_L5sWxZP0hhEas9D-f8edTm-GAqfn2-hM61txrmH5Q&oe=6AE87017", "sourceWidth": 1920, "sourceHeight": 1080, "credit": "Meta AI", "thumbnail": { "path": "/img/library/ref-f552d964d644-small.webp", "width": 480, "height": 270 } } }, "summary": "Meta’s introduction to learning video representations with V-JEPA.", "curation": { "note": null, "listingNote": "Meta’s introduction to learning video representations with V-JEPA." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "learning-transferable-visual-models-from-natural-language-supervision", "reason": "Compare video representation learning with paired image-text representation learning." }, { "id": "what-i-learned-from-competing-against-a-convnet-on-imagenet", "reason": "An image-recognition case study supplies an earlier computer-vision perspective." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/v-jepa-the-next-step-toward-advanced-machine-intelligence", "status": "curated" }, { "id": "amplifying-coding-agent-intelligence", "slug": "amplifying-coding-agent-intelligence", "source": { "title": "Amplifying | Coding agent intelligence", "type": "article", "canonicalUrl": "https://amplifying.ai/research/claude-code-picks", "authors": [ { "name": "Edwin Ong", "type": "Person" }, { "name": "Alex Vikati", "type": "Person" } ], "publisher": { "name": "Amplifying", "url": "https://amplifying.ai" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-b7a42fdb73fa.webp", "width": 1200, "height": 630, "sourcePage": "https://amplifying.ai/research/claude-code-picks", "sourceImage": "https://amplifying.ai/research/claude-code-picks/opengraph-image.png?267f044bb5256d31", "sourceWidth": 1200, "sourceHeight": 630, "credit": "Amplifying", "thumbnail": { "path": "/img/library/ref-b7a42fdb73fa-small.webp", "width": 480, "height": 252 } } }, "summary": "A study of which developer tools Claude Code selects across different tasks.", "curation": { "note": null, "listingNote": "A study of which developer tools Claude Code selects across different tasks." }, "topics": [ "AI & machine learning" ], "tags": [ "Agents" ], "related": [ { "id": "the-70-problem-hard-truths-about-ai-assisted-coding", "reason": "Tool-selection behavior complements the practical limits of AI-assisted coding." }, { "id": "model-context-protocol", "reason": "Tool access also connects to the protocols available to AI applications." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/amplifying-coding-agent-intelligence", "status": "curated" }, { "id": "lora-low-rank-adaptation-of-large-language-models", "slug": "lora-low-rank-adaptation-of-large-language-models", "source": { "title": "LoRA: Low-Rank Adaptation of Large Language Models", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2106.09685", "authors": [ { "name": "Edward J. Hu", "type": "Person" }, { "name": "Yelong Shen", "type": "Person" }, { "name": "Phillip Wallis", "type": "Person" }, { "name": "Zeyuan Allen-Zhu", "type": "Person" }, { "name": "Yuanzhi Li", "type": "Person" }, { "name": "Shean Wang", "type": "Person" }, { "name": "Lu Wang", "type": "Person" }, { "name": "Weizhu Chen", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2021-06-17", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2106.09685", "preview": { "path": "/img/library/ref-b85d9dbf9c29.webp", "width": 1200, "height": 1552, "sourcePage": "https://arxiv.org/abs/2106.09685", "sourceImage": "https://arxiv.org/pdf/2106.09685", "sourceWidth": 1237, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of LoRA: Low-Rank Adaptation of Large Language Models.", "thumbnail": { "path": "/img/library/ref-b85d9dbf9c29-small.webp", "width": 480, "height": 621 } } }, "summary": "The LoRA paper on adapting large models through low-rank parameter updates.", "curation": { "note": null, "listingNote": "The LoRA paper on adapting large models through low-rank parameter updates." }, "topics": [ "AI & machine learning" ], "tags": [ "Model adaptation" ], "related": [ { "id": "practical-tips-for-finetuning-llms-using-lora-low-rank-adaptation", "reason": "Practical experiments show how LoRA behaves during fine-tuning." }, { "id": "rank-linear-algebra", "reason": "Matrix rank is the mathematical concept underlying low-rank adaptation." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/lora-low-rank-adaptation-of-large-language-models", "status": "curated" }, { "id": "human-like-summarization-evaluation-with-chatgpt", "slug": "human-like-summarization-evaluation-with-chatgpt", "source": { "title": "Human-like Summarization Evaluation with ChatGPT", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2304.02554", "authors": [ { "name": "Mingqi Gao", "type": "Person" }, { "name": "Jie Ruan", "type": "Person" }, { "name": "Renliang Sun", "type": "Person" }, { "name": "Xunjian Yin", "type": "Person" }, { "name": "Shiping Yang", "type": "Person" }, { "name": "Xiaojun Wan", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2023-04-05", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2304.02554", "preview": { "path": "/img/library/ref-4fd703aba911.webp", "width": 1132, "height": 1600, "sourcePage": "https://arxiv.org/abs/2304.02554", "sourceImage": "https://arxiv.org/pdf/2304.02554", "sourceWidth": 1132, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Human-like Summarization Evaluation with ChatGPT.", "thumbnail": { "path": "/img/library/ref-4fd703aba911-small.webp", "width": 453, "height": 640 } } }, "summary": "Research into using ChatGPT to evaluate summaries in relation to human judgments.", "curation": { "note": null, "listingNote": "Research into using ChatGPT to evaluate summaries in relation to human judgments." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "chatgpt-as-a-factual-inconsistency-evaluator-for-text-summarization", "reason": "Compare another use of ChatGPT to assess summary factuality." }, { "id": "rouge-a-package-for-automatic-evaluation-of-summaries", "reason": "ROUGE supplies a traditional baseline for evaluating summaries." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/human-like-summarization-evaluation-with-chatgpt", "status": "curated" }, { "id": "direct-preference-optimization-your-language-model-is-secretly-a-reward-model", "slug": "direct-preference-optimization-your-language-model-is-secretly-a-reward-model", "source": { "title": "Direct Preference Optimization: Your Language Model is Secretly a Reward Model", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2305.18290v2", "authors": [ { "name": "Rafael Rafailov", "type": "Person" }, { "name": "Archit Sharma", "type": "Person" }, { "name": "Eric Mitchell", "type": "Person" }, { "name": "Stefano Ermon", "type": "Person" }, { "name": "Christopher D. Manning", "type": "Person" }, { "name": "Chelsea Finn", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2023-05-29", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2305.18290v2", "preview": { "path": "/img/library/ref-3bdad22db8b0.webp", "width": 1200, "height": 1552, "sourcePage": "https://arxiv.org/abs/2305.18290v2", "sourceImage": "https://arxiv.org/pdf/2305.18290v2", "sourceWidth": 1237, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Direct Preference Optimization: Your Language Model is Secretly a Reward Model.", "thumbnail": { "path": "/img/library/ref-3bdad22db8b0-small.webp", "width": 480, "height": 621 } } }, "summary": "A method for learning from preferences without a separate reinforcement-learning reward model.", "curation": { "note": null, "listingNote": "A method for learning from preferences without a separate reinforcement-learning reward model." }, "topics": [ "AI & machine learning" ], "tags": [ "Model adaptation" ], "related": [ { "id": "dpo-trainer", "reason": "The DPO trainer documents an implementation of the paper’s method." }, { "id": "fine-tune-mistral-7b-with-direct-preference-optimization", "reason": "A worked example applies DPO to Mistral 7B." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/direct-preference-optimization-your-language-model-is-secretly-a-reward-model", "status": "curated" }, { "id": "lost-in-the-middle-how-language-models-use-long-contexts", "slug": "lost-in-the-middle-how-language-models-use-long-contexts", "source": { "title": "Lost in the Middle: How Language Models Use Long Contexts", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2307.03172v3", "authors": [ { "name": "Nelson F. Liu", "type": "Person" }, { "name": "Kevin Lin", "type": "Person" }, { "name": "John Hewitt", "type": "Person" }, { "name": "Ashwin Paranjape", "type": "Person" }, { "name": "Michele Bevilacqua", "type": "Person" }, { "name": "Fabio Petroni", "type": "Person" }, { "name": "Percy Liang", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2023-07-06", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2307.03172v3", "preview": { "path": "/img/library/ref-9dbf664b6954.webp", "width": 1132, "height": 1600, "sourcePage": "https://arxiv.org/abs/2307.03172v3", "sourceImage": "https://arxiv.org/pdf/2307.03172v3", "sourceWidth": 1132, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Lost in the Middle: How Language Models Use Long Contexts.", "thumbnail": { "path": "/img/library/ref-9dbf664b6954-small.webp", "width": 453, "height": 640 } } }, "summary": "Research showing how the location of information affects its use in long contexts.", "curation": { "note": null, "listingNote": "Research showing how the location of information affects its use in long contexts." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "what-is-a-context-window", "reason": "Context-window basics help interpret the paper’s positional effects." }, { "id": "qwen-qwen2-5-7b-instruct-1m", "reason": "A million-token model card provides a concrete long-context comparison." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/lost-in-the-middle-how-language-models-use-long-contexts", "status": "curated" }, { "id": "end-to-end-graph-flattening-method-for-large-language-models", "slug": "end-to-end-graph-flattening-method-for-large-language-models", "source": { "title": "End-to-End Graph Flattening Method for Large Language Models", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2409.14880v1", "authors": [ { "name": "Bin Hong", "type": "Person" }, { "name": "Jinze Wu", "type": "Person" }, { "name": "Jiayu Liu", "type": "Person" }, { "name": "Liang Ding", "type": "Person" }, { "name": "Jing Sha", "type": "Person" }, { "name": "Kai Zhang", "type": "Person" }, { "name": "Shijin Wang", "type": "Person" }, { "name": "Zhenya Huang", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2024-09-23", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2409.14880v1", "preview": { "path": "/img/library/ref-06a1d7b4a08c.webp", "width": 1200, "height": 1552, "sourcePage": "https://arxiv.org/abs/2409.14880v1", "sourceImage": "https://arxiv.org/pdf/2409.14880v1", "sourceWidth": 1237, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of End-to-End Graph Flattening Method for Large Language Models.", "thumbnail": { "path": "/img/library/ref-06a1d7b4a08c-small.webp", "width": 480, "height": 621 } } }, "summary": "A method for representing graph information in a form language models can consume.", "curation": { "note": null, "listingNote": "A method for representing graph information in a form language models can consume." }, "topics": [ "Engineering" ], "tags": [ "Knowledge graphs" ], "related": [ { "id": "gpt4graph-can-large-language-models-understand-graph-structured-data-an-empirical-eva", "reason": "Graph-understanding benchmarks help motivate the need for graph representations." }, { "id": "injecting-knowledge-graphs-into-large-language-models", "reason": "Compare flattening with other methods for injecting knowledge graphs." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/end-to-end-graph-flattening-method-for-large-language-models", "status": "curated" }, { "id": "style-outweighs-substance-failure-modes-of-llm-judges-in-alignment-benchmarking", "slug": "style-outweighs-substance-failure-modes-of-llm-judges-in-alignment-benchmarking", "source": { "title": "Style Outweighs Substance: Failure Modes of LLM Judges in Alignment Benchmarking", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2409.15268v3", "authors": [ { "name": "Benjamin Feuer", "type": "Person" }, { "name": "Micah Goldblum", "type": "Person" }, { "name": "Teresa Datta", "type": "Person" }, { "name": "Sanjana Nambiar", "type": "Person" }, { "name": "Raz Besaleli", "type": "Person" }, { "name": "Samuel Dooley", "type": "Person" }, { "name": "Max Cembalest", "type": "Person" }, { "name": "John P. Dickerson", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2024-09-23", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2409.15268v3", "preview": { "path": "/img/library/ref-e3dd062dc0f7.webp", "width": 1200, "height": 1552, "sourcePage": "https://arxiv.org/abs/2409.15268v3", "sourceImage": "https://arxiv.org/pdf/2409.15268v3", "sourceWidth": 1237, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Style Outweighs Substance: Failure Modes of LLM Judges in Alignment Benchmarking.", "thumbnail": { "path": "/img/library/ref-e3dd062dc0f7-small.webp", "width": 480, "height": 621 } } }, "summary": "A study of how presentation style can bias language-model judges.", "curation": { "note": null, "listingNote": "A study of how presentation style can bias language-model judges." }, "topics": [ "AI & machine learning" ], "tags": [ "Evaluation" ], "related": [ { "id": "llm-as-judge", "reason": "The earlier LLM-judge study establishes the evaluation setting and its biases." }, { "id": "systematic-evaluation-of-llm-as-a-judge-in-llm-alignment-tasks-explainable-metrics-an", "reason": "A broader alignment-evaluation study examines prompt and metric choices." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/style-outweighs-substance-failure-modes-of-llm-judges-in-alignment-benchmarking", "status": "curated" }, { "id": "from-single-to-multi-how-llms-hallucinate-in-multi-document-summarization", "slug": "from-single-to-multi-how-llms-hallucinate-in-multi-document-summarization", "source": { "title": "From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2410.13961v1", "authors": [ { "name": "Catarina G. Belem", "type": "Person" }, { "name": "Pouya Pezeskhpour", "type": "Person" }, { "name": "Hayate Iso", "type": "Person" }, { "name": "Seiji Maekawa", "type": "Person" }, { "name": "Nikita Bhutani", "type": "Person" }, { "name": "Estevam Hruschka", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2024-10-17", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2410.13961v1", "preview": { "path": "/img/library/ref-4949c157d6c6.webp", "width": 1132, "height": 1600, "sourcePage": "https://arxiv.org/abs/2410.13961v1", "sourceImage": "https://arxiv.org/pdf/2410.13961v1", "sourceWidth": 1132, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization.", "thumbnail": { "path": "/img/library/ref-4949c157d6c6-small.webp", "width": 453, "height": 640 } } }, "summary": "An investigation of hallucinations when language models summarize multiple documents.", "curation": { "note": null, "listingNote": "An investigation of hallucinations when language models summarize multiple documents." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "extrinsic-hallucinations-in-llms", "reason": "The hallucination survey places multi-document errors in a broader taxonomy." }, { "id": "acueval-fine-grained-hallucination-evaluation-and-correction-for-abstractive-summariz", "reason": "Claim-level evaluation offers a way to inspect factual errors in summaries." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/from-single-to-multi-how-llms-hallucinate-in-multi-document-summarization", "status": "curated" }, { "id": "failure-modes-of-llms-for-causal-reasoning-on-narratives", "slug": "failure-modes-of-llms-for-causal-reasoning-on-narratives", "source": { "title": "Failure Modes of LLMs for Causal Reasoning on Narratives", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2410.23884v2", "authors": [ { "name": "Khurram Yamin", "type": "Person" }, { "name": "Shantanu Gupta", "type": "Person" }, { "name": "Gaurav R. Ghosal", "type": "Person" }, { "name": "Zachary C. Lipton", "type": "Person" }, { "name": "Bryan Wilder", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2024-10-31", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2410.23884v2", "preview": { "path": "/img/library/ref-72d328928e33.webp", "width": 1200, "height": 1552, "sourcePage": "https://arxiv.org/abs/2410.23884v2", "sourceImage": "https://arxiv.org/pdf/2410.23884v2", "sourceWidth": 1237, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Failure Modes of LLMs for Causal Reasoning on Narratives.", "thumbnail": { "path": "/img/library/ref-72d328928e33-small.webp", "width": 480, "height": 621 } } }, "summary": "A study of failures in causal reasoning about narrative information.", "curation": { "note": null, "listingNote": "A study of failures in causal reasoning about narrative information." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "gpt4graph-can-large-language-models-understand-graph-structured-data-an-empirical-eva", "reason": "Compare causal reasoning over narratives with evaluation of graph-structured understanding." }, { "id": "style-outweighs-substance-failure-modes-of-llm-judges-in-alignment-benchmarking", "reason": "A separate failure-mode study shows how presentation can mislead model-based judgments." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/failure-modes-of-llms-for-causal-reasoning-on-narratives", "status": "curated" }, { "id": "injecting-knowledge-graphs-into-large-language-models", "slug": "injecting-knowledge-graphs-into-large-language-models", "source": { "title": "Injecting Knowledge Graphs into Large Language Models", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2505.07554v1", "authors": [ { "name": "Erica Coppolillo", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2025-05-12", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2505.07554v1", "preview": { "path": "/img/library/ref-5e727bba7325.webp", "width": 1200, "height": 1552, "sourcePage": "https://arxiv.org/abs/2505.07554v1", "sourceImage": "https://arxiv.org/pdf/2505.07554v1", "sourceWidth": 1237, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Injecting Knowledge Graphs into Large Language Models.", "thumbnail": { "path": "/img/library/ref-5e727bba7325-small.webp", "width": 480, "height": 621 } } }, "summary": "Research on bringing structured knowledge graphs into language models.", "curation": { "note": null, "listingNote": "Research on bringing structured knowledge graphs into language models." }, "topics": [ "Engineering" ], "tags": [ "Knowledge graphs" ], "related": [ { "id": "end-to-end-graph-flattening-method-for-large-language-models", "reason": "Graph flattening is one way to make structured information consumable by language models." }, { "id": "knowledge-graph-deep-learning-a-case-study-in-question-answering-in-aviation-safety-d", "reason": "The aviation question-answering case study provides a domain-specific application." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/injecting-knowledge-graphs-into-large-language-models", "status": "curated" }, { "id": "forewarned-is-forearmed-a-survey-on-large-language-model-based-agents-in-autonomous-c", "slug": "forewarned-is-forearmed-a-survey-on-large-language-model-based-agents-in-autonomous-c", "source": { "title": "Forewarned is Forearmed: A Survey on Large Language Model-based Agents in Autonomous Cyberattacks", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2505.12786v2", "authors": [ { "name": "Minrui Xu", "type": "Person" }, { "name": "Jiani Fan", "type": "Person" }, { "name": "Xinyu Huang", "type": "Person" }, { "name": "Conghao Zhou", "type": "Person" }, { "name": "Jiawen Kang", "type": "Person" }, { "name": "Dusit Niyato", "type": "Person" }, { "name": "Shiwen Mao", "type": "Person" }, { "name": "Zhu Han", "type": "Person" }, { "name": "Kwok-Yan Lam", "type": "Person" }, { "name": "Xuemin Shen", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2025-05-19", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2505.12786v2", "preview": { "path": "/img/library/ref-71f41ac422fc.webp", "width": 1200, "height": 1552, "sourcePage": "https://arxiv.org/abs/2505.12786v2", "sourceImage": "https://arxiv.org/pdf/2505.12786v2", "sourceWidth": 1237, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Forewarned is Forearmed: A Survey on Large Language Model-based Agents in Autonomous Cyberattacks.", "thumbnail": { "path": "/img/library/ref-71f41ac422fc-small.webp", "width": 480, "height": 621 } } }, "summary": "A survey of autonomous cyberattacks carried out using language-model agents.", "curation": { "note": null, "listingNote": "A survey of autonomous cyberattacks carried out using language-model agents." }, "topics": [ "AI & machine learning", "Security & defense" ], "tags": [ "Agents", "Security" ], "related": [ { "id": "anthropic-threat-report", "reason": "Compare the survey’s attack methods with observed and disrupted misuse." }, { "id": "what-are-agents", "reason": "Agent concepts explain the tool-use workflows that autonomous attacks can exploit." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/forewarned-is-forearmed-a-survey-on-large-language-model-based-agents-in-autonomous-c", "status": "curated" }, { "id": "device-edge-cooperative-fine-tuning-of-foundation-models-as-a-6g-service", "slug": "device-edge-cooperative-fine-tuning-of-foundation-models-as-a-6g-service", "source": { "title": "Device-Edge Cooperative Fine-Tuning of Foundation Models as a 6G Service", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/2310.18602", "authors": [ { "name": "Hai Wu", "type": "Person" }, { "name": "Xu Chen", "type": "Person" }, { "name": "Kaibin Huang", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2023-10-28", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/2310.18602", "preview": { "path": "/img/library/ref-f87676c974b2.webp", "width": 1132, "height": 1600, "sourcePage": "https://arxiv.org/abs/2310.18602", "sourceImage": "https://arxiv.org/pdf/2310.18602", "sourceWidth": 1132, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Device-Edge Cooperative Fine-Tuning of Foundation Models as a 6G Service.", "thumbnail": { "path": "/img/library/ref-f87676c974b2-small.webp", "width": 453, "height": 640 } } }, "summary": "Research on sharing foundation-model fine-tuning between devices and edge infrastructure.", "curation": { "note": null, "listingNote": "Research on sharing foundation-model fine-tuning between devices and edge infrastructure." }, "topics": [ "AI & machine learning" ], "tags": [ "Model adaptation" ], "related": [ { "id": "lora-low-rank-adaptation-of-large-language-models", "reason": "Low-rank adaptation offers relevant context for reducing model-adaptation requirements." }, { "id": "parameter-efficient-tuning", "reason": "Course material surveys methods that train relatively few parameters." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/device-edge-cooperative-fine-tuning-of-foundation-models-as-a-6g-service", "status": "curated" }, { "id": "efficient-and-robust-approximate-nearest-neighbor-search-using-hierarchical-navigable", "slug": "efficient-and-robust-approximate-nearest-neighbor-search-using-hierarchical-navigable", "source": { "title": "Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/1603.09320", "authors": [ { "name": "Yu. A. Malkov", "type": "Person" }, { "name": "D. A. Yashunin", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2016-03-30", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/1603.09320", "preview": { "path": "/img/library/ref-4072b69e3f23.webp", "width": 1173, "height": 1600, "sourcePage": "https://arxiv.org/abs/1603.09320", "sourceImage": "https://arxiv.org/pdf/1603.09320", "sourceWidth": 1173, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs.", "thumbnail": { "path": "/img/library/ref-4072b69e3f23-small.webp", "width": 469, "height": 640 } } }, "summary": "The HNSW paper describing graph-based approximate nearest-neighbor search.", "curation": { "note": null, "listingNote": "The HNSW paper describing graph-based approximate nearest-neighbor search." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Retrieval", "Knowledge graphs" ], "related": [ { "id": "hierarchical-navigable-small-worlds-hnsw-pinecone", "reason": "An illustrated HNSW explanation complements the original paper." }, { "id": "filterable-hnsw-without-recall-loss", "reason": "Filtered HNSW extends the discussion to constrained vector search." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/efficient-and-robust-approximate-nearest-neighbor-search-using-hierarchical-navigable", "status": "curated" }, { "id": "attention-is-all-you-need", "slug": "attention-is-all-you-need", "source": { "title": "Attention Is All You Need", "type": "paper", "canonicalUrl": "https://arxiv.org/abs/1706.03762", "authors": [ { "name": "Ashish Vaswani", "type": "Person" }, { "name": "Noam Shazeer", "type": "Person" }, { "name": "Niki Parmar", "type": "Person" }, { "name": "Jakob Uszkoreit", "type": "Person" }, { "name": "Llion Jones", "type": "Person" }, { "name": "Aidan N. Gomez", "type": "Person" }, { "name": "Lukasz Kaiser", "type": "Person" }, { "name": "Illia Polosukhin", "type": "Person" } ], "publisher": { "name": "arXiv", "url": "https://arxiv.org" }, "datePublished": "2017-06-12", "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://arxiv.org/pdf/1706.03762", "preview": { "path": "/img/library/ref-a1a0262a54cd.webp", "width": 1200, "height": 1552, "sourcePage": "https://arxiv.org/abs/1706.03762", "sourceImage": "https://arxiv.org/pdf/1706.03762", "sourceWidth": 1237, "sourceHeight": 1600, "credit": "Paper authors / arXiv", "kind": "paper-first-page", "alt": "First page of Attention Is All You Need.", "thumbnail": { "path": "/img/library/ref-a1a0262a54cd-small.webp", "width": 480, "height": 621 } } }, "summary": "The Transformer paper introducing an architecture built around attention.", "curation": { "note": null, "listingNote": "The Transformer paper introducing an architecture built around attention." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "transformers-visual", "reason": "A visual explanation walks through the architecture introduced in the paper." }, { "id": "bert-pre-training-of-deep-bidirectional-transformers-for-language-understanding", "reason": "BERT is a major application of Transformer-based language representation learning." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/attention-is-all-you-need", "status": "curated", "citedIn": [ { "title": "LLM : souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "generate-synthetic-data-for-evaluating-rag-systems-using-amazon-bedrock", "slug": "generate-synthetic-data-for-evaluating-rag-systems-using-amazon-bedrock", "source": { "title": "Generate synthetic data for evaluating RAG systems using Amazon Bedrock", "type": "article", "canonicalUrl": "https://aws.amazon.com/blogs/machine-learning/generate-synthetic-data-for-evaluating-rag-systems-using-amazon-bedrock/", "authors": [], "publisher": { "name": "Amazon Web Services", "url": "https://aws.amazon.com" }, "datePublished": "2024-09-23", "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://aws.amazon.com/blogs/machine-learning/generate-synthetic-data-for-evaluating-rag-systems-using-amazon-bedrock/", "credit": "Amazon Web Services", "kind": "source-image", "alt": "Synthetic RAG evaluation data workflow, from document loading to question generation and evaluation", "sourceWidth": 1292, "sourceHeight": 1173, "path": "/img/library/generate-synthetic-data-for-evaluating-rag-systems-using-amazon-bedrock.webp", "width": 1200, "height": 1089, "thumbnail": { "path": "/img/library/generate-synthetic-data-for-evaluating-rag-systems-using-amazon-bedrock-small.webp", "width": 480, "height": 436 }, "sourceImage": "https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2024/09/09/generation-overview-ML-16558.jpg" } }, "summary": "A worked method for generating synthetic evaluation data for RAG systems.", "curation": { "note": null, "listingNote": "A worked method for generating synthetic evaluation data for RAG systems." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "ragas-automated-evaluation-of-retrieval-augmented-generation", "reason": "Ragas provides an evaluation framework for the RAG test cases being generated." }, { "id": "stop-using-lgtm-few-as-a-metric-better-rag", "reason": "Larger evaluation sets address the limits of judging a few hand-inspected results." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/generate-synthetic-data-for-evaluating-rag-systems-using-amazon-bedrock", "status": "curated" }, { "id": "biome", "slug": "biome", "source": { "title": "Biome", "type": "website", "canonicalUrl": "https://biomejs.dev/", "authors": [], "publisher": { "name": "Biome", "url": "https://biomejs.dev" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-e1bda262c1bd.webp", "width": 1200, "height": 600, "sourcePage": "https://biomejs.dev/", "sourceImage": "https://biomejs.dev/img/og.png?v=2", "sourceWidth": 2160, "sourceHeight": 1080, "credit": "Biome", "thumbnail": { "path": "/img/library/ref-e1bda262c1bd-small.webp", "width": 480, "height": 240 } } }, "summary": "A development tool combining code formatting and linting.", "curation": { "note": null, "listingNote": "A development tool combining code formatting and linting." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "pre-commit", "reason": "Pre-commit hooks can organize automated checks such as formatting and linting." }, { "id": "declutter-your-javascript-typescript-projects", "reason": "Knip complements code-quality tooling by finding unused project dependencies and files." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/biome", "status": "curated" }, { "id": "best-practices-for-llm-inference-performance-monitoring", "slug": "best-practices-for-llm-inference-performance-monitoring", "source": { "title": "Best Practices for LLM Inference Performance Monitoring", "type": "article", "canonicalUrl": "https://blog.spheron.network/best-practices-for-llm-inference-performance-monitoring", "authors": [], "publisher": { "name": "Spheron Network", "url": "https://blog.spheron.network" }, "datePublished": null, "access": "public", "schemaType": "Article", "availability": "unavailable", "preview": { "sourcePage": "https://web.archive.org/web/20251221223602/https://blog.spheron.network/best-practices-for-llm-inference-performance-monitoring", "credit": "Spheron Network", "kind": "source-image", "alt": "Spheron article cover for Best Practices for LLM Inference Performance Monitoring", "sourceWidth": 2400, "sourceHeight": 1260, "path": "/img/library/best-practices-for-llm-inference-performance-monitoring.webp", "width": 1200, "height": 630, "thumbnail": { "path": "/img/library/best-practices-for-llm-inference-performance-monitoring-small.webp", "width": 480, "height": 252 }, "sourceImage": "https://cdn.hashnode.com/res/hashnode/image/upload/v1718703674533/a98b8f4d-9456-401c-8e56-795fed5b7461.png?w=1600&h=840&fit=crop&crop=entropy&auto=compress,format&format=webp" } }, "summary": "A guide to the metrics and operational practices used to monitor language-model inference.", "curation": { "note": null, "listingNote": "A guide to the metrics and operational practices used to monitor language-model inference." }, "topics": [ "Engineering" ], "tags": [ "Inference", "Observability" ], "related": [ { "id": "prometheus-and-grafana", "reason": "A concrete setup connects vLLM metrics to Prometheus and Grafana." }, { "id": "llm-inference-performance", "reason": "Performance engineering explains the latency and throughput behavior being monitored." } ], "archive": { "snapshotUrl": "https://web.archive.org/web/20251221223602/https://blog.spheron.network/best-practices-for-llm-inference-performance-monitoring" }, "detailUrl": "https://fpaupier.fr/library/best-practices-for-llm-inference-performance-monitoring", "status": "curated" }, { "id": "vllm-easy-fast-and-cheap-llm-serving-with-pagedattention", "slug": "vllm-easy-fast-and-cheap-llm-serving-with-pagedattention", "source": { "title": "vLLM: Easy, Fast, and Cheap LLM Serving with PagedAttention", "type": "article", "canonicalUrl": "https://vllm.ai/blog/2023-06-20-vllm", "authors": [ { "name": "vLLM Team", "type": "Organization" } ], "publisher": { "name": "vllm.ai", "url": "https://vllm.ai" }, "datePublished": "2023-06-20", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-3756838033df.webp", "width": 1200, "height": 630, "sourcePage": "https://vllm.ai/blog/2023-06-20-vllm", "sourceImage": "https://vllm.ai/og?title=vLLM%3A%20Easy%2C%20Fast%2C%20and%20Cheap%20LLM%20Serving%20with%20PagedAttention&authors=Woosuk%20Kwon*%2C%20Zhuohan%20Li*%2C%20Siyuan%20Zhuang%2C%20Ying%20Sheng%2C%20Lianmin%20Zheng%2C%20Cody%20Yu%2C%20Joey%20Gonzalez%2C%20Hao%20Zhang%2C%20and%20Ion%20Stoica%20(*%20Equal%20Contribution)&date=2023-06-20&path=/blog", "sourceWidth": 1200, "sourceHeight": 630, "credit": "blog.vllm.ai", "thumbnail": { "path": "/img/library/ref-3756838033df-small.webp", "width": 480, "height": 252 } } }, "summary": "The original vLLM announcement and its approach to memory-efficient model serving.", "curation": { "note": null, "listingNote": "The original vLLM announcement and its approach to memory-efficient model serving." }, "topics": [ "Engineering" ], "tags": [ "Inference" ], "related": [ { "id": "pagedattention", "reason": "The PagedAttention paper provides the technical detail behind the announcement." }, { "id": "vllm-v1-a-major-upgrade-to-vllm-s-core-architecture", "reason": "The V1 announcement shows how the serving architecture subsequently evolved." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/vllm-easy-fast-and-cheap-llm-serving-with-pagedattention", "status": "curated" }, { "id": "configuration-as-code-cac-a-devops-guide-circleci", "slug": "configuration-as-code-cac-a-devops-guide-circleci", "source": { "title": "Configuration as Code (CaC): A DevOps guide - CircleCI", "type": "article", "canonicalUrl": "https://circleci.com/blog/configuration-as-code/", "authors": [], "publisher": { "name": "CircleCI", "url": "https://circleci.com" }, "datePublished": "2022-07-06", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-0ba8adb4e55b.webp", "width": 1200, "height": 675, "sourcePage": "https://circleci.com/blog/configuration-as-code/", "sourceImage": "https://images.ctfassets.net/il1yandlcjgk/7hoq5MUh3mxlCgIf7aJLYo/e4973a8450c06b470b92c359497f9cc3/2022-07-07-config-as-code-v2.jpg?w=1200&fm=jpg", "sourceWidth": 1200, "sourceHeight": 675, "credit": "CircleCI", "thumbnail": { "path": "/img/library/ref-0ba8adb4e55b-small.webp", "width": 480, "height": 270 } } }, "summary": "An introduction to managing deployment configuration as code.", "curation": { "note": null, "listingNote": "An introduction to managing deployment configuration as code." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "makefile-tutorial-by-example", "reason": "Makefiles provide an example of expressing repeatable project tasks as code." }, { "id": "pre-commit", "reason": "Shared pre-commit configuration is another small, concrete form of automation as code." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/configuration-as-code-cac-a-devops-guide-circleci", "status": "curated" }, { "id": "bfloat16-the-secret-to-high-performance-on-cloud-tpus", "slug": "bfloat16-the-secret-to-high-performance-on-cloud-tpus", "source": { "title": "BFloat16: The secret to high performance on Cloud TPUs", "type": "article", "canonicalUrl": "https://cloud.google.com/blog/products/ai-machine-learning/bfloat16-the-secret-to-high-performance-on-cloud-tpus", "authors": [], "publisher": { "name": "Google Cloud", "url": "https://cloud.google.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-aa623bf349dd.webp", "width": 1200, "height": 591, "sourcePage": "https://cloud.google.com/blog/products/ai-machine-learning/bfloat16-the-secret-to-high-performance-on-cloud-tpus", "sourceImage": "https://storage.googleapis.com/gweb-cloudblog-publish/images/01_-_AI__Machine_Learning_H1ZyZG8.max-2600x2600.jpg", "sourceWidth": 2600, "sourceHeight": 1280, "credit": "Google Cloud Blog", "thumbnail": { "path": "/img/library/ref-aa623bf349dd-small.webp", "width": 480, "height": 236 } } }, "summary": "An explanation of the bfloat16 number format used in Cloud TPUs.", "curation": { "note": null, "listingNote": "An explanation of the bfloat16 number format used in Cloud TPUs." }, "topics": [ "Engineering" ], "tags": [ "Inference" ], "related": [ { "id": "tinyml-talks-a-practical-guide-to-neural-network-quantization", "reason": "Compare bfloat16 with the wider design choices in neural-network quantization." }, { "id": "calculating-gpu-memory-for-serving-llms", "reason": "Numeric precision directly affects estimated GPU memory requirements." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/bfloat16-the-secret-to-high-performance-on-cloud-tpus", "status": "curated" }, { "id": "mistral-ai-models-gemini-enterprise-agent-platform-google-cloud-documentation", "slug": "mistral-ai-models-gemini-enterprise-agent-platform-google-cloud-documentation", "source": { "title": "Mistral AI models | Gemini Enterprise Agent Platform | Google Cloud Documentation", "type": "documentation", "canonicalUrl": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/partner-models/mistral", "authors": [], "publisher": { "name": "Google Cloud Documentation", "url": "https://docs.cloud.google.com" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-127285529cfc.webp", "width": 1200, "height": 630, "sourcePage": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/partner-models/mistral", "sourceImage": "https://docs.cloud.google.com/_static/cloud/images/social-icon-google-cloud-1200-630.png", "sourceWidth": 1200, "sourceHeight": 630, "credit": "Google Cloud Documentation", "thumbnail": { "path": "/img/library/ref-127285529cfc-small.webp", "width": 480, "height": 252 } } }, "summary": "Google Cloud documentation for supported Mistral models.", "curation": { "note": null, "listingNote": "Google Cloud documentation for supported Mistral models." }, "topics": [ "AI & machine learning" ], "tags": [ "Agents" ], "related": [ { "id": "models-overview", "reason": "Compare Google Cloud’s supported models with Mistral’s own model overview." }, { "id": "mistral-small-3-1-mistral-ai", "reason": "The Small 3.1 release announcement gives context on a particular model family." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/mistral-ai-models-gemini-enterprise-agent-platform-google-cloud-documentation", "status": "curated" }, { "id": "european-chips-act", "slug": "european-chips-act", "source": { "title": "European Chips Act", "type": "article", "canonicalUrl": "https://digital-strategy.ec.europa.eu/en/policies/european-chips-act", "authors": [], "publisher": { "name": "Shaping Europe’s digital future", "url": "https://digital-strategy.ec.europa.eu" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://digital-strategy.ec.europa.eu/en/policies/european-chips-act", "credit": "Jens Schlueter, via the European Commission", "kind": "source-image", "alt": "A gloved hand holds a silicon wafer patterned with microchips", "sourceWidth": 639, "sourceHeight": 426, "path": "/img/library/european-chips-act.webp", "width": 639, "height": 426, "thumbnail": { "path": "/img/library/european-chips-act-small.webp", "width": 480, "height": 320 }, "sourceImage": "https://digital-strategy.ec.europa.eu/sites/default/files/styles/oe_theme_small_no_crop/public/2023-09/%C2%A9%20Jens%20Schlueter%20-%20test%20structures%20of%20microchip%20on%20silicon%20wafer.jpg?itok=ZGb5rZ3-" } }, "summary": "The European Commission’s overview of the European Chips Act.", "curation": { "note": null, "listingNote": "The European Commission’s overview of the European Chips Act." }, "topics": [ "Engineering" ], "tags": [ "Semiconductors" ], "related": [ { "id": "european-chips-survey", "reason": "The European Chips Survey documents demand and supply-chain constraints." }, { "id": "fact-sheet-chips-and-science-act-will-lower-costs-create-jobs-strengthen-supply-chain", "reason": "Compare the European initiative with the US CHIPS and Science Act." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/european-chips-act", "status": "curated", "citedIn": [ { "title": "LLM : souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "semiconductors", "slug": "semiconductors", "source": { "title": "Semiconductors", "type": "article", "canonicalUrl": "https://contacttaiwan.tw/MainOne/DocList2.aspx?uid=729&pid=728&docid=344", "authors": [ { "name": "Industrial Development Bureau, MOEA", "type": "Organization" } ], "publisher": { "name": "Contact TAIWAN", "url": "https://contacttaiwan.tw" }, "datePublished": null, "access": "public", "schemaType": "Article", "dateModified": "2020-09-30", "preview": { "sourcePage": "https://contacttaiwan.tw/MainOne/DocList2.aspx?uid=729&pid=728&docid=344", "credit": "Contact TAIWAN", "kind": "screenshot", "alt": "Screenshot of Semiconductors", "sourceWidth": 1200, "sourceHeight": 780, "path": "/img/library/semiconductors.webp", "width": 1200, "height": 780, "thumbnail": { "path": "/img/library/semiconductors-small.webp", "width": 480, "height": 312 }, "capturedAt": "2026-10-13" } }, "summary": "A 2020 overview of Taiwan’s semiconductor industry, its manufacturing clusters, policy initiatives, and investment opportunities.", "curation": { "note": null, "listingNote": "A 2020 overview of Taiwan’s semiconductor industry, its manufacturing clusters, policy initiatives, and investment opportunities." }, "topics": [ "Engineering" ], "tags": [ "Semiconductors" ], "related": [ { "id": "semiconductor-resilience", "reason": "A global supply-chain report provides context for Taiwan’s semiconductor industry." }, { "id": "chip-war", "reason": "The history of chip competition places Taiwan’s manufacturing role in a geopolitical context." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/semiconductors", "status": "curated", "citedIn": [ { "title": "LLM : souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "ann-search-is-super-slow", "slug": "ann-search-is-super-slow", "source": { "title": "ANN Search is super slow", "type": "article", "canonicalUrl": "https://discuss.elastic.co/t/ann-search-is-super-slow/344863", "authors": [], "publisher": { "name": "Discuss the Elastic Stack", "url": "https://discuss.elastic.co" }, "datePublished": "2023-10-12", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-b72f7e4c0007.webp", "width": 512, "height": 512, "sourcePage": "https://discuss.elastic.co/t/ann-search-is-super-slow/344863", "sourceImage": "https://us1.discourse-cdn.com/elastic/original/3X/5/4/5461df8fd2fe783981b0180332821184b729980e.png", "sourceWidth": 512, "sourceHeight": 512, "credit": "Discuss the Elastic Stack", "thumbnail": { "path": "/img/library/ref-b72f7e4c0007-small.webp", "width": 480, "height": 480 } } }, "summary": "A technical discussion of slow approximate-nearest-neighbor search in Elasticsearch.", "curation": { "note": null, "listingNote": "A technical discussion of slow approximate-nearest-neighbor search in Elasticsearch." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "guidelines-to-choose-an-index", "reason": "Index-selection guidance helps frame practical nearest-neighbor performance problems." }, { "id": "efficient-and-robust-approximate-nearest-neighbor-search-using-hierarchical-navigable", "reason": "The HNSW paper explains a graph-based approach behind many vector-search systems." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/ann-search-is-super-slow", "status": "curated" }, { "id": "create-your-first-java-based-simulation", "slug": "create-your-first-java-based-simulation", "source": { "title": "Create your first Java-based simulation", "type": "documentation", "canonicalUrl": "https://docs.gatling.io/tutorials/test-as-code/java-jvm/running-your-first-simulation/", "authors": [], "publisher": { "name": "Gatling documentation", "url": "https://docs.gatling.io" }, "datePublished": "2023-12-16", "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://docs.gatling.io/tutorials/test-as-code/java-jvm/running-your-first-simulation/", "credit": "Gatling documentation", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "Gatling tutorial introducing a first Java simulation and its prerequisites.", "sourceWidth": 720, "sourceHeight": 800, "path": "/img/library/create-your-first-java-based-simulation.webp", "width": 720, "height": 800, "thumbnail": { "path": "/img/library/create-your-first-java-based-simulation-small.webp", "width": 480, "height": 533 } } }, "summary": "A tutorial for creating and running a first load test with Gatling.", "curation": { "note": null, "listingNote": "A tutorial for creating and running a first load test with Gatling." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "grafana-k6", "reason": "Compare Gatling’s Java approach with k6’s load-testing workflow." }, { "id": "locustio-locust", "reason": "Locust supplies a Python-based alternative for expressing simulated user behavior." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/create-your-first-java-based-simulation", "status": "curated" }, { "id": "models-overview", "slug": "models-overview", "source": { "title": "Models Overview", "type": "documentation", "canonicalUrl": "https://docs.mistral.ai/models", "authors": [], "publisher": { "name": "docs.mistral.ai", "url": "https://docs.mistral.ai" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-2f10eabfbc61.webp", "width": 1200, "height": 630, "sourcePage": "https://docs.mistral.ai/models", "sourceImage": "https://docs.mistral.ai/api/og?title=Models+Overview&description=Mistral+develops%2C+or+makes+available%2C+open-weight+and+commercial+large+language+models.+Explore+the+full+lineup%2C+compare+benchmarks%2C+and+find+the+right+model+for+your+use+case.&eyebraw=MODELS&image=%2Fogs%2Fdocs.png&type=generic&v=a21c7a37489791df1e3ae8b6332e85b668840ebc", "sourceWidth": 1200, "sourceHeight": 630, "credit": "docs.mistral.ai", "thumbnail": { "path": "/img/library/ref-2f10eabfbc61-small.webp", "width": 480, "height": 252 } } }, "summary": "Mistral’s overview of its models and API versions.", "curation": { "note": null, "listingNote": "Mistral’s overview of its models and API versions." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "mistral-small-3-1-mistral-ai", "reason": "A specific Mistral release announcement expands the vendor’s model overview." }, { "id": "mistral-ai-models-gemini-enterprise-agent-platform-google-cloud-documentation", "reason": "Compare the model family with versions offered through Google Cloud." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/models-overview", "status": "curated" }, { "id": "nvidia-system-management-interface-nvidia-smi", "slug": "nvidia-system-management-interface-nvidia-smi", "source": { "title": "NVIDIA System Management Interface (nvidia-smi)", "type": "documentation", "canonicalUrl": "https://docs.nvidia.com/deploy/nvidia-smi/", "authors": [], "publisher": { "name": "NVIDIA", "url": "https://docs.nvidia.com" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://docs.nvidia.com/deploy/nvidia-smi/", "credit": "NVIDIA", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "NVIDIA command reference describing nvidia-smi usage, GPU monitoring and command-line options.", "sourceWidth": 1200, "sourceHeight": 850, "path": "/img/library/nvidia-system-management-interface-nvidia-smi.webp", "width": 1200, "height": 850, "thumbnail": { "path": "/img/library/nvidia-system-management-interface-nvidia-smi-small.webp", "width": 480, "height": 340 } } }, "summary": "NVIDIA’s reference for querying and managing GPU state through nvidia-smi.", "curation": { "note": null, "listingNote": "NVIDIA’s reference for querying and managing GPU state through nvidia-smi." }, "topics": [ "Engineering" ], "tags": [ "Inference" ], "related": [ { "id": "nvidia-h100-gpu", "reason": "The H100 product reference provides hardware context for the management interface." }, { "id": "calculating-gpu-memory-for-serving-llms", "reason": "Memory-budget calculations help interpret the GPU capacity being inspected." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/nvidia-system-management-interface-nvidia-smi", "status": "curated" }, { "id": "metrics-ragas", "slug": "metrics-ragas", "source": { "title": "Metrics - Ragas", "type": "documentation", "canonicalUrl": "https://docs.ragas.io/en/latest/concepts/metrics/", "authors": [], "publisher": { "name": "docs.ragas.io", "url": "https://docs.ragas.io" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-a0afa1da6f5c.webp", "width": 1200, "height": 630, "sourcePage": "https://docs.ragas.io/en/latest/concepts/metrics/", "sourceImage": "https://docs.ragas.io/en/latest/assets/images/social/concepts/metrics/index.png", "sourceWidth": 1200, "sourceHeight": 630, "credit": "docs.ragas.io", "thumbnail": { "path": "/img/library/ref-a0afa1da6f5c-small.webp", "width": 480, "height": 252 } } }, "summary": "Reference documentation for the evaluation metrics implemented in Ragas.", "curation": { "note": null, "listingNote": "Reference documentation for the evaluation metrics implemented in Ragas." }, "topics": [ "AI & machine 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"https://i.imgur.com/R2vH9VW.png", "credit": "vLLM", "kind": "source-image", "alt": "Grafana dashboard showing vLLM request, token and GPU metrics.", "sourceWidth": 4088, "sourceHeight": 1594, "path": "/img/library/prometheus-and-grafana.webp", "width": 1200, "height": 468, "thumbnail": { "path": "/img/library/prometheus-and-grafana-small.webp", "width": 480, "height": 187 } } }, "summary": "A guide to observing vLLM with Prometheus and Grafana.", "curation": { "note": null, "listingNote": "A guide to observing vLLM with Prometheus and Grafana." }, "topics": [ "Engineering" ], "tags": [ "Inference", "Observability" ], "related": [ { "id": "prometheus-monitoring-system-time-series-database", "reason": "Prometheus is the metrics system used by this observability setup." }, { "id": "full-stack-observability-for-the-agentic-era-grafana-labs", "reason": "Grafana provides the dashboard layer configured in the example." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/prometheus-and-grafana", "status": "curated" }, { "id": "tensorize-vllm-model", "slug": "tensorize-vllm-model", "source": { "title": "Tensorize vLLM Model", "type": "documentation", "canonicalUrl": "https://docs.vllm.ai/en/stable/examples/features/tensorize_vllm_model/", "authors": [], "publisher": { "name": "vLLM", "url": "https://docs.vllm.ai" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://docs.vllm.ai/en/stable/examples/features/tensorize_vllm_model/", "credit": "vLLM", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "vLLM Tensorizer example with Python code for serializing and loading language models.", "sourceWidth": 688, "sourceHeight": 800, "path": "/img/library/tensorize-vllm-model.webp", "width": 688, "height": 800, "thumbnail": { "path": "/img/library/tensorize-vllm-model-small.webp", "width": 480, "height": 558 } } }, "summary": "An example of serializing and loading vLLM models with Tensorizer.", "curation": { "note": null, "listingNote": "An example of serializing and loading vLLM models with Tensorizer." }, "topics": [ "Engineering" ], "tags": [ "Inference" ], "related": [ { "id": "vllm-project-vllm", "reason": "The vLLM source repository contains the serving engine used by the example." }, { "id": "using-object-storage-with-s3cmd", "reason": "S3 object-storage tooling complements workflows that store serialized model files remotely." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/tensorize-vllm-model", "status": "curated" }, { "id": "multimodal-inputs", "slug": "multimodal-inputs", "source": { "title": "Multimodal Inputs", "type": "documentation", "canonicalUrl": "https://docs.vllm.ai/en/stable/features/multimodal_inputs/", "authors": [], "publisher": { "name": "vLLM", "url": "https://docs.vllm.ai" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://docs.vllm.ai/en/stable/features/multimodal_inputs/", "credit": "vLLM", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "vLLM guide explaining multimodal inputs with offline inference and image-input examples.", "sourceWidth": 688, "sourceHeight": 800, "path": "/img/library/multimodal-inputs.webp", "width": 688, "height": 800, "thumbnail": { "path": "/img/library/multimodal-inputs-small.webp", "width": 480, "height": 558 } } }, "summary": "Documentation for sending multimodal inputs to vLLM.", "curation": { "note": null, "listingNote": "Documentation for sending multimodal inputs to vLLM." }, "topics": [ "Engineering" ], "tags": [ "Inference" ], "related": [ { "id": "colpali-efficient-document-retrieval-with-vision-language-models", "reason": "Vision-language document retrieval provides a concrete multimodal workload." }, { "id": "learning-transferable-visual-models-from-natural-language-supervision", "reason": "CLIP provides background on representations that connect images and text." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/multimodal-inputs", "status": "curated" }, { "id": "offline-inference-vllm", "slug": "offline-inference-vllm", "source": { "title": "Offline Inference - vLLM", "type": "documentation", "canonicalUrl": "https://docs.vllm.ai/en/stable/serving/offline_inference/", "authors": [], "publisher": { "name": "vLLM", "url": "https://docs.vllm.ai" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://docs.vllm.ai/en/stable/serving/offline_inference/", "credit": "vLLM", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "vLLM offline inference reference showing supported models and the generative API.", "sourceWidth": 688, "sourceHeight": 800, "path": "/img/library/offline-inference-vllm.webp", "width": 688, "height": 800, "thumbnail": { "path": "/img/library/offline-inference-vllm-small.webp", "width": 480, "height": 558 } } }, "summary": "Reference documentation for offline inference with vLLM.", "curation": { "note": null, "listingNote": "Reference documentation for offline inference with vLLM." }, "topics": [ "Engineering" ], "tags": [ "Inference" ], "related": [ { "id": "vllm-project-vllm", "reason": "The vLLM repository supplies the engine used for offline inference." }, { "id": "text-generation-inference", "reason": "Compare batch-oriented local execution with a dedicated text-generation server." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/offline-inference-vllm", "status": "curated" }, { "id": "usage-stats-collection", "slug": "usage-stats-collection", "source": { "title": "Usage Stats Collection", "type": "documentation", "canonicalUrl": "https://docs.vllm.ai/en/stable/usage/usage_stats/", "authors": [], "publisher": { "name": "vLLM", "url": "https://docs.vllm.ai" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://docs.vllm.ai/en/stable/usage/usage_stats/", "credit": "vLLM", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "vLLM usage-statistics documentation showing collected information and how to opt out.", "sourceWidth": 688, "sourceHeight": 800, "path": "/img/library/usage-stats-collection.webp", "width": 688, "height": 800, "thumbnail": { "path": "/img/library/usage-stats-collection-small.webp", "width": 480, "height": 558 } } }, "summary": "The usage data collected by vLLM and the options for disabling collection.", "curation": { "note": null, "listingNote": "The usage data collected by vLLM and the options for disabling collection." }, "topics": [ "Engineering" ], "tags": [ "Inference" ], "related": [ { "id": "vllm-project-vllm", "reason": "The serving-engine repository provides context for the telemetry described here." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/usage-stats-collection", "status": "curated" }, { "id": "frequently-asked-questions", "slug": "frequently-asked-questions", "source": { "title": "Frequently Asked Questions", "type": "documentation", "canonicalUrl": "https://docs.vllm.ai/en/v0.5.1/serving/faq.html", "authors": [], "publisher": { "name": "vLLM", "url": "https://docs.vllm.ai" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://docs.vllm.ai/en/v0.5.1/serving/faq.html", "credit": "vLLM", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "vLLM FAQ answering questions about OpenAI API compatibility and embedding models.", "sourceWidth": 960, "sourceHeight": 510, "path": "/img/library/frequently-asked-questions.webp", "width": 960, "height": 510, "thumbnail": { "path": "/img/library/frequently-asked-questions-small.webp", "width": 480, "height": 255 } } }, "summary": "Frequently asked questions about operating vLLM.", "curation": { "note": null, "listingNote": "Frequently asked questions about operating vLLM." }, "topics": [ "Engineering" ], "tags": [ "Inference" ], "related": [ { "id": "vllm-project-vllm", "reason": "The vLLM repository is the implementation behind these operational questions." }, { "id": "vllm-v1-a-major-upgrade-to-vllm-s-core-architecture", "reason": "The architecture update adds context for how the serving engine evolved." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/frequently-asked-questions", "status": "curated" }, { "id": "structured-outputs", "slug": "structured-outputs", "source": { "title": "Structured Outputs", "type": "documentation", "canonicalUrl": "https://docs.vllm.ai/en/v0.8.2/features/structured_outputs.html", "authors": [], "publisher": { "name": "vLLM", "url": "https://docs.vllm.ai" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://docs.vllm.ai/en/v0.8.2/features/structured_outputs.html", "credit": "vLLM", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "vLLM structured-output documentation introducing guided generation through the OpenAI-compatible API.", "sourceWidth": 1126, "sourceHeight": 800, "path": "/img/library/structured-outputs.webp", "width": 1126, "height": 800, "thumbnail": { "path": "/img/library/structured-outputs-small.webp", "width": 480, "height": 341 } } }, "summary": "Documentation for constraining generated outputs to a required structure.", "curation": { "note": null, "listingNote": "Documentation for constraining generated outputs to a required structure." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "text-generation", "reason": "Structured output adds constraints to the broader text-generation workflow." }, { "id": "ai-engineer-2023-keynote-building-blocks-for-llm-systems", "reason": "Place output constraints alongside the other building blocks of an LLM system." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/structured-outputs", "status": "curated" }, { "id": "vector-database-management-systems-fundamental-concepts-use-cases-and-current-challen", "slug": "vector-database-management-systems-fundamental-concepts-use-cases-and-current-challen", "source": { "title": "Vector database management systems: Fundamental concepts, use-cases, and current challenges", "type": "paper", "canonicalUrl": "https://doi.org/10.1016/j.cogsys.2024.101216", "authors": [ { "name": "Toni Taipalus", "type": "Person" } ], "publisher": { "name": "Elsevier BV", "url": "https://doi.org" }, "datePublished": null, "access": "public", "schemaType": "ScholarlyArticle", "publicationYear": 2024, "doi": "10.1016/j.cogsys.2024.101216", "preview": { "sourcePage": "https://doi.org/10.1016/j.cogsys.2024.101216", "sourceImage": "https://dbs-research.github.io/pdf/2024_vector.pdf", "credit": "Toni Taipalus", "kind": "paper-first-page", "alt": "Title page of Vector database management systems: Fundamental concepts, use-cases, and current challenges.", "sourceWidth": 1351, "sourceHeight": 1800, "path": "/img/library/vector-database-management-systems-fundamental-concepts-use-cases-and-current-challen.webp", "width": 1200, "height": 1599, "thumbnail": { "path": "/img/library/vector-database-management-systems-fundamental-concepts-use-cases-and-current-challen-small.webp", "width": 480, "height": 640 }, "caption": "First page of the published paper, from the author’s research site." } }, "summary": "An overview of vector database concepts, use cases, and implementation challenges.", "curation": { "note": null, "listingNote": "An overview of vector database concepts, use cases, and implementation challenges." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval" ], "related": [ { "id": "what-is-milvus", "reason": "Milvus provides a concrete vector-database architecture to compare with the survey." }, { "id": "pgvector-pgvector", "reason": "pgvector offers a contrasting approach based on extending PostgreSQL." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/vector-database-management-systems-fundamental-concepts-use-cases-and-current-challen", "status": "curated" }, { "id": "carousel-personalization-in-music-streaming-apps-with-contextual-bandits", "slug": "carousel-personalization-in-music-streaming-apps-with-contextual-bandits", "source": { "title": "Carousel Personalization in Music Streaming Apps with Contextual Bandits", "type": "paper", "canonicalUrl": "https://doi.org/10.1145/3383313.3412217", "authors": [ { "name": "Walid Bendada", "type": "Person" }, { "name": "Guillaume Salha", "type": "Person" }, { "name": "Théo Bontempelli", "type": "Person" } ], "publisher": { "name": "ACM", "url": "https://doi.org" }, "datePublished": "2020-09-22", "access": "public", "schemaType": "ScholarlyArticle", "doi": "10.1145/3383313.3412217", "preview": { "sourcePage": "https://doi.org/10.1145/3383313.3412217", "sourceImage": "https://arxiv.org/pdf/2009.06546", "credit": "Walid Bendada, Guillaume Salha, Théo Bontempelli", "kind": "paper-first-page", "alt": "Title page of Carousel Personalization in Music Streaming Apps with Contextual Bandits.", "sourceWidth": 1391, "sourceHeight": 1800, "path": "/img/library/carousel-personalization-in-music-streaming-apps-with-contextual-bandits.webp", "width": 1200, "height": 1553, "thumbnail": { "path": "/img/library/carousel-personalization-in-music-streaming-apps-with-contextual-bandits-small.webp", "width": 480, "height": 621 }, "caption": "First page of the authors’ arXiv manuscript." } }, "summary": "Research on personalizing music-app carousels using contextual bandits.", "curation": { "note": null, "listingNote": "Research on personalizing music-app carousels using contextual bandits." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "music-recommendation-at-spotify", "reason": "The Spotify presentation places carousel personalization within the broader recommender system." }, { "id": "a-framework-for-offline-evaluation-of-recommender-systems-based-on-probabilistic-rela", "reason": "An offline-evaluation framework complements the study of recommendation decisions." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/carousel-personalization-in-music-streaming-apps-with-contextual-bandits", "status": "curated" }, { "id": "betweenness-centrality", "slug": "betweenness-centrality", "source": { "title": "Betweenness centrality", "type": "documentation", "canonicalUrl": "https://en.wikipedia.org/wiki/Betweenness_centrality", "authors": [], "publisher": { "name": "Wikipedia", "url": "https://en.wikipedia.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-139eff8a38ff.webp", "width": 1200, "height": 1200, "sourcePage": "https://en.wikipedia.org/wiki/Betweenness_centrality", "sourceImage": "https://thumb.wikimedia.org/wikipedia/commons/thumb/6/60/Graph_betweenness.svg/1280px-Graph_betweenness.svg.png?utm_source=en.wikipedia.org&utm_campaign=index&utm_content=thumbnail", "sourceWidth": 1280, "sourceHeight": 1280, "credit": "Wikipedia", "thumbnail": { "path": "/img/library/ref-139eff8a38ff-small.webp", "width": 480, "height": 480 } } }, "summary": "An explanation of betweenness centrality in graph analysis.", "curation": { "note": null, "listingNote": "An explanation of betweenness centrality in graph analysis." }, "topics": [ "Engineering" ], "tags": [ "Knowledge graphs" ], "related": [ { "id": "pagerank", "reason": "Compare centrality based on shortest paths with PageRank’s link-based measure." }, { "id": "louvain-communities", "reason": "Community detection is another graph-analysis operation available in NetworkX." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/betweenness-centrality", "status": "curated" }, { "id": "binary-search", "slug": "binary-search", "source": { "title": "Binary search", "type": "documentation", "canonicalUrl": "https://en.wikipedia.org/wiki/Binary_search", "authors": [], "publisher": { "name": "Wikipedia", "url": "https://en.wikipedia.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-d68eb442207d.webp", "width": 1200, "height": 511, "sourcePage": "https://en.wikipedia.org/wiki/Binary_search", "sourceImage": "https://thumb.wikimedia.org/wikipedia/commons/thumb/8/83/Binary_Search_Depiction.svg/1280px-Binary_Search_Depiction.svg.png?utm_source=en.wikipedia.org&utm_campaign=index&utm_content=thumbnail", "sourceWidth": 1280, "sourceHeight": 545, "credit": "Wikipedia", "thumbnail": { "path": "/img/library/ref-d68eb442207d-small.webp", "width": 480, "height": 204 } } }, "summary": "The binary-search algorithm and its computational properties.", "curation": { "note": null, "listingNote": "The binary-search algorithm and its computational properties." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "introduction-information-retrieval", "reason": "A retrieval textbook provides broader context for search algorithms and indexing." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/binary-search", "status": "curated" }, { "id": "confusion-matrix", "slug": "confusion-matrix", "source": { "title": "Confusion matrix", "type": "documentation", "canonicalUrl": "https://en.wikipedia.org/wiki/Confusion_matrix", "authors": [], "publisher": { "name": "Wikipedia", "url": "https://en.wikipedia.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://en.wikipedia.org/wiki/Confusion_matrix", "credit": "Wikipedia contributors", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "Wikipedia explanation of confusion matrices and how predicted classes compare with true labels.", "sourceWidth": 972, "sourceHeight": 900, "path": "/img/library/confusion-matrix.webp", "width": 972, "height": 900, "thumbnail": { "path": "/img/library/confusion-matrix-small.webp", "width": 480, "height": 444 } } }, "summary": "A reference on interpreting classification outcomes with a confusion matrix.", "curation": { "note": null, "listingNote": "A reference on interpreting classification outcomes with a confusion matrix." }, "topics": [ "AI & machine learning" ], "tags": [ "Evaluation" ], "related": [ { "id": "scikit-learn-metrics-reference", "reason": "The scikit-learn reference supplies implementations of classification metrics." }, { "id": "carrying-out-error-analysis", "reason": "Error analysis turns classification outcomes into improvement priorities." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/confusion-matrix", "status": "curated" }, { "id": "discounted-cumulative-gain", "slug": "discounted-cumulative-gain", "source": { "title": "Discounted cumulative gain", "type": "documentation", "canonicalUrl": "https://en.wikipedia.org/wiki/Discounted_cumulative_gain", "authors": [], "publisher": { "name": "Wikipedia", "url": "https://en.wikipedia.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://en.wikipedia.org/wiki/Discounted_cumulative_gain", "credit": "Wikipedia contributors", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "Wikipedia introduction to discounted cumulative gain and its use in ranking evaluation.", "sourceWidth": 972, "sourceHeight": 900, "path": "/img/library/discounted-cumulative-gain.webp", "width": 972, "height": 900, "thumbnail": { "path": "/img/library/discounted-cumulative-gain-small.webp", "width": 480, "height": 444 } } }, "summary": "An explanation of discounted cumulative gain for ranking evaluation.", "curation": { "note": null, "listingNote": "An explanation of discounted cumulative gain for ranking evaluation." }, "topics": [ "AI & machine learning" ], "tags": [ "Evaluation" ], "related": [ { "id": "mean-reciprocal-rank", "reason": "Compare graded ranking quality with reciprocal rank of the first relevant result." }, { "id": "introduction-information-retrieval", "reason": "The textbook places both metrics in a retrieval-evaluation workflow." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/discounted-cumulative-gain", "status": "curated" }, { "id": "dog-whistle-politics", "slug": "dog-whistle-politics", "source": { "title": "Dog whistle (politics)", "type": "documentation", "canonicalUrl": "https://en.wikipedia.org/wiki/Dog_whistle_(politics)", "authors": [], "publisher": { "name": "Wikipedia", "url": "https://en.wikipedia.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://en.wikipedia.org/wiki/Dog_whistle_(politics)", "credit": "Wikipedia contributors", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "Wikipedia introduction to dog-whistle politics and the origin of the term.", "sourceWidth": 972, "sourceHeight": 900, "path": "/img/library/dog-whistle-politics.webp", "width": 972, "height": 900, "thumbnail": { "path": "/img/library/dog-whistle-politics-small.webp", "width": 480, "height": 444 } } }, "summary": "Background on coded political messaging and its interpretation.", "curation": { "note": null, "listingNote": "Background on coded political messaging and its interpretation." }, "topics": [ "Security & defense" ], "tags": [ "Security" ], "related": [ { "id": "anthropic-threat-report", "reason": "Compare coded political messaging with documented misuse of AI in influence and surveillance operations." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/dog-whistle-politics", "status": "curated" }, { "id": "gartner-hype-cycle", "slug": "gartner-hype-cycle", "source": { "title": "Gartner hype cycle", "type": "documentation", "canonicalUrl": "https://en.wikipedia.org/wiki/Gartner_hype_cycle", "authors": [], "publisher": { "name": "Wikipedia", "url": "https://en.wikipedia.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-4ca84a07caf4.webp", "width": 1200, "height": 781, "sourcePage": "https://en.wikipedia.org/wiki/Gartner_hype_cycle", "sourceImage": "https://thumb.wikimedia.org/wikipedia/commons/thumb/9/94/Gartner_Hype_Cycle.svg/1280px-Gartner_Hype_Cycle.svg.png?utm_source=en.wikipedia.org&utm_campaign=index&utm_content=thumbnail", "sourceWidth": 1280, "sourceHeight": 833, "credit": "Wikipedia", "thumbnail": { "path": "/img/library/ref-4ca84a07caf4-small.webp", "width": 480, "height": 312 } } }, "summary": "An explanation of the Gartner hype-cycle model of technology expectations.", "curation": { "note": null, "listingNote": "An explanation of the Gartner hype-cycle model of technology expectations." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "how-people-create-and-destroy-value-with-generative-ai", "reason": "Measured knowledge-work outcomes provide evidence to compare with technology expectations." }, { "id": "ai-essentials-for-tech-executives", "reason": "Technology leaders need to connect adoption decisions with the maturity of AI capabilities." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/gartner-hype-cycle", "status": "curated" }, { "id": "likert-scale", "slug": "likert-scale", "source": { "title": "Likert scale", "type": "documentation", "canonicalUrl": "https://en.wikipedia.org/wiki/Likert_scale", "authors": [], "publisher": { "name": "Wikipedia", "url": "https://en.wikipedia.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-1d3d569e577e.webp", "width": 1200, "height": 466, "sourcePage": "https://en.wikipedia.org/wiki/Likert_scale", "sourceImage": "https://thumb.wikimedia.org/wikipedia/commons/thumb/e/e4/Social_Network_Diagram_%28segment%29.svg/1280px-Social_Network_Diagram_%28segment%29.svg.png?utm_source=en.wikipedia.org&utm_campaign=index&utm_content=thumbnail", "sourceWidth": 1280, "sourceHeight": 497, "credit": "Wikipedia", "thumbnail": { "path": "/img/library/ref-1d3d569e577e-small.webp", "width": 480, "height": 186 } } }, "summary": "A reference on Likert scales for collecting and interpreting judgments.", "curation": { "note": null, "listingNote": "A reference on Likert scales for collecting and interpreting judgments." }, "topics": [ "AI & machine learning" ], "tags": [ "Evaluation" ], "related": [ { "id": "feedback-functions", "reason": "Human judgments are one source of feedback for evaluating AI applications." }, { "id": "spearman-s-rank-correlation-coefficient", "reason": "Rank correlation offers a way to compare ordered judgments." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/likert-scale", "status": "curated" }, { "id": "rank-linear-algebra", "slug": "rank-linear-algebra", "source": { "title": "Rank (linear algebra)", "type": "documentation", "canonicalUrl": "https://en.wikipedia.org/wiki/Rank_(linear_algebra)", "authors": [], "publisher": { "name": "Wikipedia", "url": "https://en.wikipedia.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://en.wikipedia.org/wiki/Rank_(linear_algebra)", "credit": "Wikipedia contributors", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "Wikipedia introduction to matrix rank, row space, column space and formal definitions.", "sourceWidth": 972, "sourceHeight": 900, "path": "/img/library/rank-linear-algebra.webp", "width": 972, "height": 900, "thumbnail": { "path": "/img/library/rank-linear-algebra-small.webp", "width": 480, "height": 444 } } }, "summary": "An explanation of matrix rank in linear algebra.", "curation": { "note": null, "listingNote": "An explanation of matrix rank in linear algebra." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "lora-low-rank-adaptation-of-large-language-models", "reason": "Low-rank adaptation applies the matrix-rank concept to language-model tuning." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/rank-linear-algebra", "status": "curated" }, { "id": "spearman-s-rank-correlation-coefficient", "slug": "spearman-s-rank-correlation-coefficient", "source": { "title": "Spearman's rank correlation coefficient", "type": "documentation", "canonicalUrl": "https://en.wikipedia.org/wiki/Spearman's_rank_correlation_coefficient", "authors": [], "publisher": { "name": "Wikipedia", "url": "https://en.wikipedia.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-4429fa47b36c.webp", "width": 1200, "height": 1138, "sourcePage": "https://en.wikipedia.org/wiki/Spearman's_rank_correlation_coefficient", "sourceImage": "https://thumb.wikimedia.org/wikipedia/commons/thumb/4/4e/Spearman_fig1.svg/1280px-Spearman_fig1.svg.png?utm_source=en.wikipedia.org&utm_campaign=index&utm_content=thumbnail", "sourceWidth": 1280, "sourceHeight": 1214, "credit": "Wikipedia", "thumbnail": { "path": "/img/library/ref-4429fa47b36c-small.webp", "width": 480, "height": 455 } } }, "summary": "A reference on rank correlation and how it measures monotonic association.", "curation": { "note": null, "listingNote": "A reference on rank correlation and how it measures monotonic association." }, "topics": [ "AI & machine learning" ], "tags": [ "Evaluation" ], "related": [ { "id": "likert-scale", "reason": "Likert scales supply ordered judgments that may be compared through rank association." }, { "id": "llm-as-judge", "reason": "Agreement between model judges and human preferences is a practical evaluation concern." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/spearman-s-rank-correlation-coefficient", "status": "curated" }, { "id": "faiss-a-library-for-efficient-similarity-search", "slug": "faiss-a-library-for-efficient-similarity-search", "source": { "title": "Faiss: A library for efficient similarity search", "type": "article", "canonicalUrl": "https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/", "authors": [], "publisher": { "name": "Engineering at Meta", "url": "https://engineering.fb.com" }, "datePublished": "2017-03-29", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-478211174b10.webp", "width": 960, "height": 600, "sourcePage": "https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/", "sourceImage": "https://engineering.fb.com/wp-content/uploads/2017/03/faiss_logo.png", "sourceWidth": 960, "sourceHeight": 600, "credit": "Engineering at Meta", "thumbnail": { "path": "/img/library/ref-478211174b10-small.webp", "width": 480, "height": 300 } } }, "summary": "Meta’s introduction to the Faiss library for vector similarity search.", "curation": { "note": null, "listingNote": "Meta’s introduction to the Faiss library for vector similarity search." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval" ], "related": [ { "id": "guidelines-to-choose-an-index", "reason": "The Faiss index-selection guide follows naturally from the library introduction." }, { "id": "product-quantization-for-nearest-neighbor-search", "reason": "Product quantization explains one family of compact vector-search representations." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/faiss-a-library-for-efficient-similarity-search", "status": "curated" }, { "id": "ai-engineer-2023-keynote-building-blocks-for-llm-systems", "slug": "ai-engineer-2023-keynote-building-blocks-for-llm-systems", "source": { "title": "AI Engineer 2023 Keynote - Building Blocks for LLM Systems", "type": "article", "canonicalUrl": "https://eugeneyan.com/speaking/ai-eng-summit/", "authors": [ { "name": "Eugene Yan", "type": "Person" } ], "publisher": { "name": "eugeneyan.com", "url": "https://eugeneyan.com" }, "datePublished": "2023-10-09", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-fce65e918eeb.webp", "width": 1200, "height": 630, "sourcePage": "https://eugeneyan.com/speaking/ai-eng-summit/", "sourceImage": "https://eugeneyan.com/assets/og_image/ai-eng-summit.jpg", "sourceWidth": 1200, "sourceHeight": 630, "credit": "eugeneyan.com", "thumbnail": { "path": "/img/library/ref-fce65e918eeb-small.webp", "width": 480, "height": 252 } } }, "summary": "A keynote on evaluation, retrieval, guardrails, and feedback in LLM systems.", "curation": { "note": null, "listingNote": "A keynote on evaluation, retrieval, guardrails, and feedback in LLM systems." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "building-a-generative-ai-platform", "reason": "A platform-architecture article provides another view of the same system building blocks." }, { "id": "applied-llms", "reason": "Practitioner lessons connect those components to delivery experience." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/ai-engineer-2023-keynote-building-blocks-for-llm-systems", "status": "curated" }, { "id": "deepfake-macron-creez-aisement-une-video-de-macron-par-l-ia", "slug": "deepfake-macron-creez-aisement-une-video-de-macron-par-l-ia", "source": { "title": "Deepfake Macron : Créez aisément une vidéo de Macron par l'IA", "type": "article", "canonicalUrl": "https://fr.vidnoz.com/intelligence-artificielle/deepfake-macron.html", "authors": [], "publisher": { "name": "Vidnoz", "url": "https://fr.vidnoz.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "availability": "unavailable", "preview": { "sourcePage": "https://web.archive.org/web/20240623091551/https://fr.vidnoz.com/intelligence-artificielle/deepfake-macron.html", "sourceImage": "https://fr.vidnoz.com/bimg/deepfake-macron.webp", "credit": "Vidnoz", "kind": "source-image", "caption": "Original article illustration, labeled as a deepfake by Vidnoz.", "alt": "Vidnoz article graphic labeled Deepfake Macron, showing a manipulated likeness of Emmanuel Macron.", "sourceWidth": 700, "sourceHeight": 400, "path": "/img/library/deepfake-macron-creez-aisement-une-video-de-macron-par-l-ia.webp", "width": 700, "height": 400, "thumbnail": { "path": "/img/library/deepfake-macron-creez-aisement-une-video-de-macron-par-l-ia-small.webp", "width": 480, "height": 274 } } }, "summary": "An example of consumer software for generating synthetic video of a public figure.", "curation": { "note": null, "listingNote": "An example of consumer software for generating synthetic video of a public figure." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "anthropic-threat-report", "reason": "Compare synthetic-media tooling with reported real-world misuse of AI." }, { "id": "v-jepa-the-next-step-toward-advanced-machine-intelligence", "reason": "Video representation learning offers technical context from another part of computer vision." } ], "archive": { "snapshotUrl": "https://web.archive.org/web/20240623091551/https://fr.vidnoz.com/intelligence-artificielle/deepfake-macron.html" }, "detailUrl": "https://fpaupier.fr/library/deepfake-macron-creez-aisement-une-video-de-macron-par-l-ia", "status": "curated" }, { "id": "lithographie", "slug": "lithographie", "source": { "title": "Lithographie", "type": "documentation", "canonicalUrl": "https://fr.wikipedia.org/wiki/Lithographie", "authors": [], "publisher": { "name": "Wikipédia", "url": "https://fr.wikipedia.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-bbb32acd9fe2.webp", "width": 960, "height": 1383, "sourcePage": "https://fr.wikipedia.org/wiki/Lithographie", "sourceImage": "https://thumb.wikimedia.org/wikipedia/commons/thumb/c/c9/Centenaire_de_la_lithographie_par_Puvis_de_Chavannes_1895.jpg/960px-Centenaire_de_la_lithographie_par_Puvis_de_Chavannes_1895.jpg?utm_source=fr.wikipedia.org&utm_campaign=index&utm_content=thumbnail", "sourceWidth": 960, "sourceHeight": 1383, "credit": "Wikipédia", "thumbnail": { "path": "/img/library/ref-bbb32acd9fe2-small.webp", "width": 444, "height": 640 } } }, "summary": "Background on lithography and its role in producing patterned surfaces.", "curation": { "note": null, "listingNote": "Background on lithography and its role in producing patterned surfaces." }, "topics": [ "Engineering" ], "tags": [ "Knowledge graphs", "Semiconductors" ], "related": [ { "id": "asml-euv-lithography-systems", "reason": "Contrast lithographic printing with the extreme-ultraviolet systems used for chip manufacturing." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/lithographie", "status": "curated", "citedIn": [ { "title": "LLM : souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "quantile", "slug": "quantile", "source": { "title": "Quantile", "type": "documentation", "canonicalUrl": "https://fr.wikipedia.org/wiki/Quantile", "authors": [], "publisher": { "name": "Wikipédia", "url": "https://fr.wikipedia.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-961c8f1b0a7b.webp", "width": 742, "height": 428, "sourcePage": "https://fr.wikipedia.org/wiki/Quantile", "sourceImage": "https://upload.wikimedia.org/wikipedia/commons/5/5e/Iqr_with_quantile.png?utm_source=fr.wikipedia.org&utm_campaign=index&utm_content=thumbnail_unscaled", "sourceWidth": 742, "sourceHeight": 428, "credit": "Wikipédia", "thumbnail": { "path": "/img/library/ref-961c8f1b0a7b-small.webp", "width": 480, "height": 277 } } }, "summary": "An explanation of quantiles for understanding distributions.", "curation": { "note": null, "listingNote": "An explanation of quantiles for understanding distributions." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "normal-distribution-percentile-formula-graph-vaia", "reason": "A worked normal-distribution example makes percentile calculations concrete." }, { "id": "spearman-s-rank-correlation-coefficient", "reason": "Rank correlation offers another statistical tool for interpreting ordered data." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/quantile", "status": "curated" }, { "id": "bitsandbytes-foundation-bitsandbytes", "slug": "bitsandbytes-foundation-bitsandbytes", "source": { "title": "bitsandbytes-foundation/bitsandbytes", "type": "repository", "canonicalUrl": "https://github.com/bitsandbytes-foundation/bitsandbytes", "authors": [], "publisher": { "name": "GitHub", "url": "https://github.com" }, "datePublished": null, "access": "public", "schemaType": "SoftwareSourceCode", "preview": { "path": "/img/library/ref-50601649b71e.webp", "width": 1024, "height": 1024, "sourcePage": "https://github.com/bitsandbytes-foundation/bitsandbytes", "sourceImage": "https://repository-images.githubusercontent.com/373674258/6f2ff326-b5d0-46e3-8809-2afa0b260766", "sourceWidth": 1024, "sourceHeight": 1024, "credit": "GitHub", "thumbnail": { "path": "/img/library/ref-50601649b71e-small.webp", "width": 480, "height": 480 } } }, "summary": "A library for low-bit model operations and quantization.", "curation": { "note": null, "listingNote": "A library for low-bit model operations and quantization." }, "topics": [ "AI & machine learning" ], "tags": [ "Model adaptation" ], "related": [ { "id": "making-llms-even-more-accessible-with-bitsandbytes-4-bit-quantization-and-qlora", "reason": "The tutorial demonstrates 4-bit loading and QLoRA with this library." }, { "id": "tinyml-talks-a-practical-guide-to-neural-network-quantization", "reason": "The quantization talk provides conceptual background for low-bit operations." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/bitsandbytes-foundation-bitsandbytes", "status": "curated" }, { "id": "huggingface-sentence-transformers", "slug": "huggingface-sentence-transformers", "source": { "title": "huggingface/sentence-transformers", "type": "repository", "canonicalUrl": "https://github.com/huggingface/sentence-transformers", "authors": [], "publisher": { "name": "GitHub", "url": "https://github.com" }, "datePublished": null, "access": "public", "schemaType": "SoftwareSourceCode", "preview": { "path": "/img/library/ref-26d89cb2e0ed.webp", "width": 1200, "height": 600, "sourcePage": "https://github.com/huggingface/sentence-transformers", "sourceImage": "https://opengraph.githubassets.com/a7de1ded26a5004b6d307015cc6a8f5ee52e8dedb79825c9c43914cc0ddae9e2/huggingface/sentence-transformers", "sourceWidth": 1200, "sourceHeight": 600, "credit": "GitHub", "thumbnail": { "path": "/img/library/ref-26d89cb2e0ed-small.webp", "width": 480, "height": 240 } } }, "summary": "A library for training and using embedding and reranking models.", "curation": { "note": null, "listingNote": "A library for training and using embedding and reranking models." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval" ], "related": [ { "id": "training-and-finetuning-embedding-models-with-sentence-transformers", "reason": "The training guide shows how to adapt the library’s embedding models." }, { "id": "loss-overview", "reason": "The loss-function guide helps choose an objective for that training." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/huggingface-sentence-transformers", "status": "curated" }, { "id": "confident-ai-deepeval", "slug": "confident-ai-deepeval", "source": { "title": "confident-ai/deepeval", "type": "repository", "canonicalUrl": "https://github.com/confident-ai/deepeval", "authors": [], "publisher": { "name": "GitHub", "url": "https://github.com" }, "datePublished": null, "access": "public", "schemaType": "SoftwareSourceCode", "preview": { "path": "/img/library/ref-c26d01773103.webp", "width": 1200, "height": 600, "sourcePage": "https://github.com/confident-ai/deepeval", "sourceImage": "https://opengraph.githubassets.com/445c4c943ef7bc3c4228b69408ada0774933120c7f9f4f32ef66ce8dcd4e8b86/confident-ai/deepeval", "sourceWidth": 1200, "sourceHeight": 600, "credit": "GitHub", "thumbnail": { "path": "/img/library/ref-c26d01773103-small.webp", "width": 480, "height": 240 } } }, "summary": "A framework for evaluating language-model applications.", "curation": { "note": null, "listingNote": "A framework for evaluating language-model applications." }, "topics": [ "AI & machine learning" ], "tags": [ "Evaluation" ], "related": [ { "id": "ai-product-evals", "reason": "Product-specific checks provide context for using an evaluation framework." }, { "id": "feedback-functions", "reason": "Compare different feedback-function families for assessing application runs." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/confident-ai-deepeval", "status": "curated" }, { "id": "guidelines-to-choose-an-index", "slug": "guidelines-to-choose-an-index", "source": { "title": "Guidelines to choose an index", "type": "documentation", "canonicalUrl": "https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index", "authors": [], "publisher": { "name": "GitHub", "url": "https://github.com" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-164d045b9f2f.webp", "width": 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"slug": "locustio-locust", "source": { "title": "locustio/locust", "type": "repository", "canonicalUrl": "https://github.com/locustio/locust", "authors": [], "publisher": { "name": "GitHub", "url": "https://github.com" }, "datePublished": null, "access": "public", "schemaType": "SoftwareSourceCode", "preview": { "path": "/img/library/ref-90065d2b62e3.webp", "width": 1200, "height": 600, "sourcePage": "https://github.com/locustio/locust", "sourceImage": "https://opengraph.githubassets.com/d12a8c4f86a408c205b8ba263616af933f0e651061578039705e739998b395b5/locustio/locust", "sourceWidth": 1200, "sourceHeight": 600, "credit": "GitHub", "thumbnail": { "path": "/img/library/ref-90065d2b62e3-small.webp", "width": 480, "height": 240 } } }, "summary": "A load-testing tool that defines user behavior in Python.", "curation": { "note": null, "listingNote": "A load-testing tool that defines user behavior in Python." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "grafana-k6", "reason": "Compare Python-defined load scenarios with the k6 testing tool." }, { "id": "create-your-first-java-based-simulation", "reason": "A Gatling tutorial supplies another way to create a first load test." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/locustio-locust", "status": "curated" }, { "id": "model-context-protocol", "slug": "model-context-protocol", "source": { "title": "Model Context Protocol", "type": "website", "canonicalUrl": "https://github.com/modelcontextprotocol", "authors": [], "publisher": { "name": "GitHub", "url": "https://github.com" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "sourcePage": "https://github.com/modelcontextprotocol", "credit": "Model Context Protocol contributors / GitHub", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "Model Context Protocol GitHub organization overview with project documentation and getting-started links.", 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"full-stack-observability-for-the-agentic-era-grafana-labs", "source": { "title": "Full-stack observability for the agentic era | Grafana Labs", "type": "website", "canonicalUrl": "https://grafana.com/", "authors": [], "publisher": { "name": "Grafana Labs", "url": "https://grafana.com" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-de984c481bc4.webp", "width": 1200, "height": 630, "sourcePage": "https://grafana.com/", "sourceImage": "https://a-us.storyblok.com/f/1022730/1920x1080/f5f38546c4/image-homepage-metaimage.png/m/1200x630/filters:quality(80)/", "sourceWidth": 1200, "sourceHeight": 630, "credit": "Grafana Labs", "thumbnail": { "path": "/img/library/ref-de984c481bc4-small.webp", "width": 480, "height": 252 } } }, "summary": "The Grafana observability platform and its technical resources.", "curation": { "note": null, "listingNote": "The Grafana observability platform and its technical resources." }, "topics": [ 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"type": "Person" } ], "publisher": { "name": "HAL", "url": "https://hal.science" }, "datePublished": "2017-12-18", "access": "public", "schemaType": "ScholarlyArticle", "preview": { "sourcePage": "https://hal.science/hal-01666117v1", "sourceImage": "https://web.archive.org/web/20241202172103id_/https://hal.science/hal-01666117v1/document", "credit": "Rajani Chulyadyo, Philippe Leray", "kind": "document-page", "alt": "Title page of A Framework for Offline Evaluation of Recommender Systems based on Probabilistic Relational Models.", "sourceWidth": 1391, "sourceHeight": 1800, "path": "/img/library/a-framework-for-offline-evaluation-of-recommender-systems-based-on-probabilistic-rela.webp", "width": 1200, "height": 1553, "thumbnail": { "path": "/img/library/a-framework-for-offline-evaluation-of-recommender-systems-based-on-probabilistic-rela-small.webp", "width": 480, "height": 621 }, "pageNumber": 2, "caption": "First page of the paper in the HAL manuscript, retrieved from the Internet Archive." } }, "summary": "A framework for evaluating recommender systems that use relational data.", "curation": { "note": null, "listingNote": "A framework for evaluating recommender systems that use relational data." }, "topics": [ "AI & machine learning" ], "tags": [ "Evaluation" ], "related": [ { "id": "carousel-personalization-in-music-streaming-apps-with-contextual-bandits", "reason": "The carousel-personalization paper supplies a concrete recommender-system use case." }, { "id": "music-recommendation-at-spotify", "reason": "The Spotify presentation places evaluation within a deployed recommendation system." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/a-framework-for-offline-evaluation-of-recommender-systems-based-on-probabilistic-rela", "status": "curated" }, { "id": "object-storage-utiliser-object-storage-avec-s3cmd-ovhcloud-documentation", "slug": "object-storage-utiliser-object-storage-avec-s3cmd-ovhcloud-documentation", "source": { "title": "Object Storage - Utiliser Object Storage avec S3cmd - OVHcloud Documentation", "type": "documentation", "canonicalUrl": "https://docs.ovhcloud.com/fr/guides/storage-and-backup/object-storage/s3-s3cmd", "authors": [], "publisher": { "name": "docs.ovhcloud.com", "url": "https://docs.ovhcloud.com" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://docs.ovhcloud.com/fr/guides/storage-and-backup/object-storage/s3-s3cmd", "credit": "docs.ovhcloud.com", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "OVHcloud documentation explaining how to manage object-storage buckets with S3cmd.", "sourceWidth": 712, "sourceHeight": 900, "path": "/img/library/object-storage-utiliser-object-storage-avec-s3cmd-ovhcloud-documentation.webp", "width": 712, "height": 900, "thumbnail": { "path": "/img/library/object-storage-utiliser-object-storage-avec-s3cmd-ovhcloud-documentation-small.webp", "width": 480, "height": 607 } } }, "summary": "OVHcloud instructions for configuring S3cmd with object storage.", "curation": { "note": null, "listingNote": "OVHcloud instructions for configuring S3cmd with object storage." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "using-object-storage-with-s3cmd", "reason": "Compare S3cmd configuration for OVHcloud with the equivalent Scaleway workflow." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/object-storage-utiliser-object-storage-avec-s3cmd-ovhcloud-documentation", "status": "curated" }, { "id": "nousresearch-hermes-4-70b", "slug": "nousresearch-hermes-4-70b", "source": { "title": "NousResearch/Hermes-4-70B", "type": "website", "canonicalUrl": "https://huggingface.co/NousResearch/Hermes-4-70B", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-2e90814e4332.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/NousResearch/Hermes-4-70B", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/models/NousResearch/Hermes-4-70B.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-2e90814e4332-small.webp", "width": 480, "height": 259 } } }, "summary": "The model card for Nous Research’s Hermes 4 70B.", "curation": { "note": null, "listingNote": "The model card for Nous Research’s Hermes 4 70B." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "model-cards", "reason": "Model-card guidance explains how to interpret intended uses, evaluations, and limitations." }, { "id": "comparing-open-language-models", "reason": "A comparison of open models offers another lens for model selection." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/nousresearch-hermes-4-70b", "status": "curated", "citedIn": [ { "title": "LLM : souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "qwen-qwen2-5-7b-instruct-1m", "slug": "qwen-qwen2-5-7b-instruct-1m", "source": { "title": "Qwen/Qwen2.5-7B-Instruct-1M", "type": "website", "canonicalUrl": "https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-1M", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-2efbbacc2b73.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-1M", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/models/Qwen/Qwen2.5-7B-Instruct-1M.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-2efbbacc2b73-small.webp", "width": 480, "height": 259 } } }, "summary": "The model card for Qwen2.5 7B Instruct with a million-token context window.", "curation": { "note": null, "listingNote": "The model card for Qwen2.5 7B Instruct with a million-token context window." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "lost-in-the-middle-how-language-models-use-long-contexts", "reason": "Long-context research examines whether available information is actually used reliably." }, { "id": "what-is-a-context-window", "reason": "Context-window basics explain the capacity advertised by this model card." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/qwen-qwen2-5-7b-instruct-1m", "status": "curated" }, { "id": "thebloke-codellama-7b-gguf", "slug": "thebloke-codellama-7b-gguf", "source": { "title": "TheBloke/CodeLlama-7B-GGUF", "type": "website", "canonicalUrl": "https://huggingface.co/TheBloke/CodeLlama-7B-GGUF", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-16c0b77ad488.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/TheBloke/CodeLlama-7B-GGUF", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/models/TheBloke/CodeLlama-7B-GGUF.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-16c0b77ad488-small.webp", "width": 480, "height": 259 } } }, "summary": "A GGUF distribution of Code Llama 7B with quantized variants.", "curation": { "note": null, "listingNote": "A GGUF distribution of Code Llama 7B with quantized variants." }, "topics": [ "AI & machine learning" ], "tags": [ "Model adaptation" ], "related": [ { "id": "quantize-llama-models-with-gguf-and-llama-cpp", "reason": "The GGUF guide explains the quantized model format distributed here." }, { "id": "k-quants-by-ikawrakow-pull-request-1684-ggml-org-llama-cpp", "reason": "The k-quants discussion provides implementation context for llama.cpp quantization." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/thebloke-codellama-7b-gguf", "status": "curated" }, { "id": "making-llms-even-more-accessible-with-bitsandbytes-4-bit-quantization-and-qlora", "slug": "making-llms-even-more-accessible-with-bitsandbytes-4-bit-quantization-and-qlora", "source": { "title": "Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA", "type": "article", "canonicalUrl": "https://huggingface.co/blog/4bit-transformers-bitsandbytes", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-b4a7c3005f3c.webp", "width": 1200, "height": 600, "sourcePage": "https://huggingface.co/blog/4bit-transformers-bitsandbytes", "sourceImage": "https://huggingface.co/blog/assets/96_hf_bitsandbytes_integration/Thumbnail_blue.png", "sourceWidth": 1300, "sourceHeight": 650, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-b4a7c3005f3c-small.webp", "width": 480, "height": 240 } } }, "summary": "A guide to 4-bit model loading and QLoRA with bitsandbytes.", "curation": { "note": null, "listingNote": "A guide to 4-bit model loading and QLoRA with bitsandbytes." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Model adaptation", "Inference" ], "related": [ { "id": "bitsandbytes-foundation-bitsandbytes", "reason": "The bitsandbytes repository contains the low-bit operations used by the tutorial." }, { "id": "lora-low-rank-adaptation-of-large-language-models", "reason": "LoRA provides the adaptation method combined with quantization in QLoRA." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/making-llms-even-more-accessible-with-bitsandbytes-4-bit-quantization-and-qlora", "status": "curated" }, { "id": "train-400x-faster-static-embedding-models-with-sentence-transformers", "slug": "train-400x-faster-static-embedding-models-with-sentence-transformers", "source": { "title": "Train 400x faster Static Embedding Models with Sentence Transformers", "type": "article", "canonicalUrl": "https://huggingface.co/blog/static-embeddings", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-fd842f7b4e7b.webp", "width": 1200, "height": 675, "sourcePage": "https://huggingface.co/blog/static-embeddings", "sourceImage": "https://huggingface.co/blog/assets/train-sentence-transformers/st-hf-thumbnail.png", "sourceWidth": 1920, "sourceHeight": 1080, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-fd842f7b4e7b-small.webp", "width": 480, "height": 270 } } }, "summary": "A method for efficiently training static text embeddings.", "curation": { "note": null, "listingNote": "A method for efficiently training static text embeddings." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval" ], "related": [ { "id": "training-and-finetuning-embedding-models-with-sentence-transformers", "reason": "Compare efficient static embeddings with the broader embedding-training workflow." }, { "id": "mteb-leaderboard-a-hugging-face-space-by-mteb", "reason": "MTEB provides a benchmark resource for comparing embedding quality." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/train-400x-faster-static-embedding-models-with-sentence-transformers", "status": "curated" }, { "id": "training-and-finetuning-embedding-models-with-sentence-transformers", "slug": "training-and-finetuning-embedding-models-with-sentence-transformers", "source": { "title": "Training and Finetuning Embedding Models with Sentence Transformers", "type": "article", "canonicalUrl": "https://huggingface.co/blog/train-sentence-transformers", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-dc0ae972b39a.webp", "width": 1200, "height": 675, "sourcePage": "https://huggingface.co/blog/train-sentence-transformers", "sourceImage": "https://huggingface.co/blog/assets/train-sentence-transformers/st-hf-thumbnail.png", "sourceWidth": 1920, "sourceHeight": 1080, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-dc0ae972b39a-small.webp", "width": 480, "height": 270 } } }, "summary": "Guidance for training and adapting Sentence Transformers embedding models.", "curation": { "note": null, "listingNote": "Guidance for training and adapting Sentence Transformers embedding models." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Model adaptation" ], "related": [ { "id": "loss-overview", "reason": "Choose a training objective using the Sentence Transformers loss overview." }, { "id": "huggingface-sentence-transformers", "reason": "The implementation repository contains the models and training tools discussed here." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/training-and-finetuning-embedding-models-with-sentence-transformers", "status": "curated" }, { "id": "huggingfaceh4-instruction-dataset", "slug": "huggingfaceh4-instruction-dataset", "source": { "title": "HuggingFaceH4/instruction-dataset", "type": "website", "canonicalUrl": "https://huggingface.co/datasets/HuggingFaceH4/instruction-dataset", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-eba0fda40445.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/datasets/HuggingFaceH4/instruction-dataset", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/datasets/HuggingFaceH4/instruction-dataset.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-eba0fda40445-small.webp", "width": 480, "height": 259 } } }, "summary": "An instruction-tuning dataset hosted by Hugging Face H4.", "curation": { "note": null, "listingNote": "An instruction-tuning dataset hosted by Hugging Face H4." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "bigscience-xp3all", "reason": "Compare a second collection of prompted data for instruction tuning." }, { "id": "aligning-language-models-to-follow-instructions", "reason": "Instruction-following research explains the behavior these datasets aim to support." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/huggingfaceh4-instruction-dataset", "status": "curated" }, { "id": "chaoticneutrals-creative-writing-sharegpt", "slug": "chaoticneutrals-creative-writing-sharegpt", "source": { "title": "ChaoticNeutrals/Creative_Writing-ShareGPT", "type": "website", "canonicalUrl": "https://huggingface.co/datasets/ChaoticNeutrals/Creative_Writing-ShareGPT", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-05903ef5b69f.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/datasets/ChaoticNeutrals/Creative_Writing-ShareGPT", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/datasets/ChaoticNeutrals/Creative_Writing-ShareGPT.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-05903ef5b69f-small.webp", "width": 480, "height": 259 } } }, "summary": "A ShareGPT-format creative-writing dataset used as an example of fine-tuning data.", "curation": { "note": null, "listingNote": "A ShareGPT-format creative-writing dataset used as an example of fine-tuning data." }, "topics": [ "AI & machine learning" ], "tags": [ "Model adaptation" ], "related": [ { "id": "huggingfaceh4-instruction-dataset", "reason": "Compare a domain-specific ShareGPT dataset with a general instruction dataset." }, { "id": "easily-train-a-specialized-llm-peft-lora-qlora-llama-adapter-and-more", "reason": "A fine-tuning guide explains how specialized data can be used for model adaptation." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/chaoticneutrals-creative-writing-sharegpt", "status": "curated" }, { "id": "sentence-transformers-gooaq", "slug": "sentence-transformers-gooaq", "source": { "title": "sentence-transformers/gooaq", "type": "website", "canonicalUrl": "https://huggingface.co/datasets/sentence-transformers/gooaq", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-fc7e61484c11.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/datasets/sentence-transformers/gooaq", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/datasets/sentence-transformers/gooaq.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-fc7e61484c11-small.webp", "width": 480, "height": 259 } } }, "summary": "The GooAQ question-answering dataset in Sentence Transformers format.", "curation": { "note": null, "listingNote": "The GooAQ question-answering dataset in Sentence Transformers format." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "training-and-finetuning-embedding-models-with-sentence-transformers", "reason": "The training guide shows how question-answer data can support embedding adaptation." }, { "id": "sentence-transformers-msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1", "reason": "Compare a second retrieval-oriented dataset in the Sentence Transformers ecosystem." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/sentence-transformers-gooaq", "status": "curated" }, { "id": "sentence-transformers-msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1", "slug": "sentence-transformers-msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1", "source": { "title": "sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1", "type": "website", "canonicalUrl": "https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-f9b66af3559b.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-f9b66af3559b-small.webp", "width": 480, "height": 259 } } }, "summary": "A Sentence Transformers training dataset derived from MS MARCO.", "curation": { "note": null, "listingNote": "A Sentence Transformers training dataset derived from MS MARCO." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "training-and-finetuning-embedding-models-with-sentence-transformers", "reason": "The training guide provides context for using this retrieval dataset." }, { "id": "loss-overview", "reason": "Loss-function choices connect training data with embedding objectives." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/sentence-transformers-msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1", "status": "curated" }, { "id": "downloading-models", "slug": "downloading-models", "source": { "title": "Downloading models", "type": "documentation", "canonicalUrl": "https://huggingface.co/docs/hub/models-downloading", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-13b283164d2d.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/docs/hub/models-downloading", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/docs/hub/models-downloading.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-13b283164d2d-small.webp", "width": 480, "height": 259 } } }, "summary": "Instructions for downloading model files from Hugging Face.", "curation": { "note": null, "listingNote": "Instructions for downloading model files from Hugging Face." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "command-line-interface-cli", "reason": "The command-line interface offers a practical route to downloading Hub model files." }, { "id": "text-generation-inference", "reason": "Serving documentation provides a next step after obtaining model artifacts." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/downloading-models", "status": "curated" }, { "id": "command-line-interface-cli", "slug": "command-line-interface-cli", "source": { "title": "Command Line Interface (CLI)", "type": "documentation", "canonicalUrl": "https://huggingface.co/docs/huggingface_hub/guides/cli", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-e6429534ed7f.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/docs/huggingface_hub/guides/cli", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/docs/huggingface_hub/guides/cli.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-e6429534ed7f-small.webp", "width": 480, "height": 259 } } }, "summary": "Reference documentation for the Hugging Face command-line interface.", "curation": { "note": null, "listingNote": "Reference documentation for the Hugging Face command-line interface." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "downloading-models", "reason": "The model-download guide focuses on one task supported by Hub tooling." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/command-line-interface-cli", "status": "curated" }, { "id": "peft", "slug": "peft", "source": { "title": "PEFT", "type": "documentation", "canonicalUrl": "https://huggingface.co/docs/peft/index", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-07292347f439.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/docs/peft/index", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/docs/peft/index.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-07292347f439-small.webp", "width": 480, "height": 259 } } }, "summary": "Documentation for the PEFT parameter-efficient fine-tuning library.", "curation": { "note": null, "listingNote": "Documentation for the PEFT parameter-efficient fine-tuning library." }, "topics": [ "AI & machine learning" ], "tags": [ "Model adaptation" ], "related": [ { "id": "easily-train-a-specialized-llm-peft-lora-qlora-llama-adapter-and-more", "reason": "The adaptation guide introduces the techniques implemented by PEFT." }, { "id": "lora-low-rank-adaptation-of-large-language-models", "reason": "The LoRA paper explains a central parameter-efficient tuning method." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/peft", "status": "curated" }, { "id": "text-generation-inference", "slug": "text-generation-inference", "source": { "title": "Text Generation Inference", "type": "documentation", "canonicalUrl": "https://huggingface.co/docs/text-generation-inference/en/index", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://huggingface.co/docs/text-generation-inference/en/index", "sourceImage": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/TGI.png", "credit": "Hugging Face", "kind": "source-image", "alt": "Text Generation Inference architecture showing the HTTP server, router and model servers.", "sourceWidth": 7178, "sourceHeight": 4390, "path": "/img/library/text-generation-inference.webp", "width": 1200, "height": 734, "thumbnail": { "path": "/img/library/text-generation-inference-small.webp", "width": 480, "height": 294 } } }, "summary": "Documentation for Hugging Face Text Generation Inference.", "curation": { "note": null, "listingNote": "Documentation for Hugging Face Text Generation Inference." }, "topics": [ "Engineering" ], "tags": [ "Inference" ], "related": [ { "id": "huggingface-text-generation-inference", "reason": "The source repository implements the documented inference server." }, { "id": "migrating-from-openai-to-open-llms-using-tgis-messages-api", "reason": "The migration guide demonstrates TGI’s Messages API in use." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/text-generation-inference", "status": "curated" }, { "id": "supported-models", "slug": "supported-models", "source": { "title": "Supported Models", "type": "documentation", "canonicalUrl": "https://huggingface.co/docs/text-generation-inference/supported_models", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://huggingface.co/docs/text-generation-inference/supported_models", "credit": "Hugging Face", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "Hugging Face Text Generation Inference documentation listing supported language-model families.", "sourceWidth": 644, "sourceHeight": 900, "path": "/img/library/supported-models.webp", "width": 644, "height": 900, "thumbnail": { "path": "/img/library/supported-models-small.webp", "width": 458, "height": 640 } } }, "summary": "The model architectures supported by Text Generation Inference.", "curation": { "note": null, "listingNote": "The model architectures supported by Text Generation Inference." }, "topics": [ "Engineering" ], "tags": [ "Inference" ], "related": [ { "id": "text-generation-inference", "reason": "The server documentation explains the deployment context for supported architectures." }, { "id": "huggingface-text-generation-inference", "reason": "The source repository provides the implementation of model support." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/supported-models", "status": "curated" }, { "id": "trl-transformers-reinforcement-learning", "slug": "trl-transformers-reinforcement-learning", "source": { "title": "TRL - Transformers Reinforcement Learning", "type": "documentation", "canonicalUrl": "https://huggingface.co/docs/trl/index", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-07796550e51c.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/docs/trl/index", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/docs/trl/index.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-07796550e51c-small.webp", "width": 480, "height": 259 } } }, "summary": "Documentation for the TRL reinforcement-learning and preference-training library.", "curation": { "note": null, "listingNote": "Documentation for the TRL reinforcement-learning and preference-training library." }, "topics": [ "AI & machine learning" ], "tags": [ "Model adaptation" ], "related": [ { "id": "huggingface-trl", "reason": "The repository implements the reinforcement-learning and preference-training workflows." }, { "id": "dpo-trainer", "reason": "DPO Trainer documents a specific method within that toolkit." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/trl-transformers-reinforcement-learning", "status": "curated" }, { "id": "dpo-trainer", "slug": "dpo-trainer", "source": { "title": "DPO Trainer", "type": "documentation", "canonicalUrl": "https://huggingface.co/docs/trl/dpo_trainer", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-5ca5627b3eb0.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/docs/trl/dpo_trainer", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/docs/trl/dpo_trainer.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-5ca5627b3eb0-small.webp", "width": 480, "height": 259 } } }, "summary": "Reference documentation for the direct preference optimization trainer.", "curation": { "note": null, "listingNote": "Reference documentation for the direct preference optimization trainer." }, "topics": [ "AI & machine learning" ], "tags": [ "Model adaptation" ], "related": [ { "id": "direct-preference-optimization-your-language-model-is-secretly-a-reward-model", "reason": "The paper explains the optimization method behind the trainer." }, { "id": "fine-tune-mistral-7b-with-direct-preference-optimization", "reason": "A Mistral fine-tuning tutorial demonstrates the method in practice." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/dpo-trainer", "status": "curated" }, { "id": "migrating-from-openai-to-open-llms-using-tgis-messages-api", "slug": "migrating-from-openai-to-open-llms-using-tgis-messages-api", "source": { "title": "Migrating from OpenAI to Open LLMs Using TGI’s Messages API", "type": "documentation", "canonicalUrl": "https://huggingface.co/learn/cookbook/en/tgi_messages_api_demo", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "path": "/img/library/ref-9b09b680af72.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/learn/cookbook/en/tgi_messages_api_demo", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/learn/cookbook/tgi_messages_api_demo.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-9b09b680af72-small.webp", "width": 480, "height": 259 } } }, "summary": "A guide to using open models through TGI’s Messages API.", "curation": { "note": null, "listingNote": "A guide to using open models through TGI’s Messages API." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "text-generation-inference", "reason": "The TGI documentation explains the serving system used in the migration." }, { "id": "llm-economics-which-is-cheaper-to-deploy-chatgpt-vs-open-source-llms", "reason": "Compare the economics behind choosing hosted services or self-hosted open models." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/migrating-from-openai-to-open-llms-using-tgis-messages-api", "status": "curated" }, { "id": "meta-llama-llama-2-7b-chat-hf", "slug": "meta-llama-llama-2-7b-chat-hf", "source": { "title": "meta-llama/Llama-2-7b-chat-hf", "type": "website", "canonicalUrl": "https://huggingface.co/meta-llama/Llama-2-7b-chat-hf", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-b2c081678531.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/meta-llama/Llama-2-7b-chat-hf", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/models/meta-llama/Llama-2-7b-chat-hf.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-b2c081678531-small.webp", "width": 480, "height": 259 } } }, "summary": "The model card for Meta’s Llama 2 7B chat model.", "curation": { "note": null, "listingNote": "The model card for Meta’s Llama 2 7B chat model." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "introducing-meta-llama-3-the-most-capable-openly-available-llm-to-date", "reason": "The next Llama generation provides a later reference point for this model family." }, { "id": "model-cards", "reason": "Model-card guidance helps interpret the documented capabilities and limitations." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/meta-llama-llama-2-7b-chat-hf", "status": "curated" }, { "id": "meta-llama-llama-3-2-1b", "slug": "meta-llama-llama-3-2-1b", "source": { "title": "meta-llama/Llama-3.2-1B", "type": "website", "canonicalUrl": "https://huggingface.co/meta-llama/Llama-3.2-1B", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-28b05cb23579.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/meta-llama/Llama-3.2-1B", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/models/meta-llama/Llama-3.2-1B.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-28b05cb23579-small.webp", "width": 480, "height": 259 } } }, "summary": "The model card for Meta’s Llama 3.2 1B model.", "curation": { "note": null, "listingNote": "The model card for Meta’s Llama 3.2 1B model." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "introducing-meta-llama-3-the-most-capable-openly-available-llm-to-date", "reason": "The Llama 3 announcement provides context for the wider model family." }, { "id": "model-cards", "reason": "Model-card guidance explains how to assess the release’s documented uses and limitations." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/meta-llama-llama-3-2-1b", "status": "curated" }, { "id": "mistralai-mistral-small-3-1-24b-base-2503", "slug": "mistralai-mistral-small-3-1-24b-base-2503", "source": { "title": "mistralai/Mistral-Small-3.1-24B-Base-2503", "type": "website", "canonicalUrl": "https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-9a24e3e06118.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/models/mistralai/Mistral-Small-3.1-24B-Base-2503.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-9a24e3e06118-small.webp", "width": 480, "height": 259 } } }, "summary": "The base-model card for Mistral Small 3.1 24B.", "curation": { "note": null, "listingNote": "The base-model card for Mistral Small 3.1 24B." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "mistralai-mistral-small-3-1-24b-instruct-2503", "reason": "Compare the base model with its instruction-tuned counterpart." }, { "id": "mistral-small-3-1-mistral-ai", "reason": "The announcement describes the corresponding Mistral Small 3.1 release." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/mistralai-mistral-small-3-1-24b-base-2503", "status": "curated" }, { "id": "mistralai-mistral-small-3-1-24b-instruct-2503-address-discrepancies-in-the-languages", "slug": "mistralai-mistral-small-3-1-24b-instruct-2503-address-discrepancies-in-the-languages", "source": { "title": "mistralai/Mistral-Small-3.1-24B-Instruct-2503 · Address discrepancies in the languages supported by the Mistral Small 3.1 2503", "type": "website", "canonicalUrl": "https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/54", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-98a4f284b22c.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/54", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/models/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/54.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-98a4f284b22c-small.webp", "width": 480, "height": 259 } } }, "summary": "A discussion about inconsistencies in the documented languages supported by Mistral Small 3.1.", "curation": { "note": null, "listingNote": "A discussion about inconsistencies in the documented languages supported by Mistral Small 3.1." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "mistralai-mistral-small-3-1-24b-instruct-2503", "reason": "The discussion concerns the language-support claims in this model’s own card." }, { "id": "model-cards", "reason": "Model-card guidance explains why precise capability documentation matters." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/mistralai-mistral-small-3-1-24b-instruct-2503-address-discrepancies-in-the-languages", "status": "curated" }, { "id": "mteb-leaderboard-a-hugging-face-space-by-mteb", "slug": "mteb-leaderboard-a-hugging-face-space-by-mteb", "source": { "title": "MTEB Leaderboard - a Hugging Face Space by mteb", "type": "website", "canonicalUrl": "https://huggingface.co/spaces/mteb/leaderboard", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-2705e7deddaf.webp", "width": 1200, "height": 630, "sourcePage": "https://huggingface.co/spaces/mteb/leaderboard", "sourceImage": "https://cdn-uploads.huggingface.co/production/uploads/61af4544d691b3aadd1f62b6/A56z-sYlNvd7XyeopfGhg.png", "sourceWidth": 1200, "sourceHeight": 630, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-2705e7deddaf-small.webp", "width": 480, "height": 252 } } }, "summary": "A leaderboard for comparing text-embedding models across MTEB evaluations.", "curation": { "note": null, "listingNote": "A leaderboard for comparing text-embedding models across MTEB evaluations." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "recent-advances-in-text-embedding-a-comprehensive-review-of-top-performing-methods-on", "reason": "The review discusses methods evaluated on the MTEB benchmark." }, { "id": "openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddings", "reason": "An embedding-model critique illustrates questions to ask of reported benchmark results." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/mteb-leaderboard-a-hugging-face-space-by-mteb", "status": "curated" }, { "id": "stabilityai-stable-diffusion-xl-base-1-0", "slug": "stabilityai-stable-diffusion-xl-base-1-0", "source": { "title": "stabilityai/stable-diffusion-xl-base-1.0", "type": "website", "canonicalUrl": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0", "authors": [], "publisher": { "name": "Hugging Face", "url": "https://huggingface.co" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-3a7d85803d53.webp", "width": 1200, "height": 648, "sourcePage": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0", "sourceImage": "https://cdn-thumbnails.huggingface.co/social-thumbnails/models/stabilityai/stable-diffusion-xl-base-1.0.png", "sourceWidth": 1200, "sourceHeight": 648, "credit": "Hugging Face", "thumbnail": { "path": "/img/library/ref-3a7d85803d53-small.webp", "width": 480, "height": 259 } } }, "summary": "The model card for Stable Diffusion XL 1.0.", "curation": { "note": null, "listingNote": "The model card for Stable Diffusion XL 1.0." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "model-cards", "reason": "Model-card guidance helps interpret the documented uses and limitations of an image model." }, { "id": "learning-transferable-visual-models-from-natural-language-supervision", "reason": "Vision-language representation learning provides related technical context." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/stabilityai-stable-diffusion-xl-base-1-0", "status": "curated" }, { "id": "product-quantization-for-nearest-neighbor-search", "slug": "product-quantization-for-nearest-neighbor-search", "source": { "title": "Product Quantization for Nearest Neighbor Search", "type": "paper", "canonicalUrl": "https://inria.hal.science/inria-00514462v2", "authors": [ { "name": "Hervé Jégou", "type": "Person" }, { "name": "Matthijs Douze", "type": "Person" }, { "name": "Cordelia Schmid", "type": "Person" } ], "publisher": { "name": "HAL", "url": "https://inria.hal.science" }, "datePublished": null, "access": "public", "schemaType": "ScholarlyArticle", "publicationYear": 2011, "preview": { "sourcePage": "https://inria.hal.science/inria-00514462v2", "sourceImage": "https://web.archive.org/web/20241220083645id_/https://inria.hal.science/inria-00514462v2/document", "credit": "Hervé Jégou, Matthijs Douze, Cordelia Schmid", "kind": "document-page", "alt": "Title page of Product Quantization for Nearest Neighbor Search.", "sourceWidth": 1391, "sourceHeight": 1800, "path": "/img/library/product-quantization-for-nearest-neighbor-search.webp", "width": 1200, "height": 1553, "thumbnail": { "path": "/img/library/product-quantization-for-nearest-neighbor-search-small.webp", "width": 480, "height": 621 }, "pageNumber": 2, "caption": "First page of the paper in the HAL postprint, retrieved from the Internet Archive." } }, "summary": "Research on compact vector representations for approximate nearest-neighbor search.", "curation": { "note": null, "listingNote": "Research on compact vector representations for 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"topic-extraction-with-non-negative-matrix-factorization-and-latent-dirichlet-allocati", "reason": "Compare embedding-based topic modeling with NMF and latent Dirichlet allocation." }, { "id": "huggingface-sentence-transformers", "reason": "Sentence Transformers supplies a related toolkit for learning and using text embeddings." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/bertopic", "status": "curated" }, { "id": "practical-tips-for-finetuning-llms-using-lora-low-rank-adaptation", "slug": "practical-tips-for-finetuning-llms-using-lora-low-rank-adaptation", "source": { "title": "Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation)", "type": "article", "canonicalUrl": "https://magazine.sebastianraschka.com/p/practical-tips-for-finetuning-llms", "authors": [ { "name": "Sebastian Raschka, PhD", "type": "Person" } ], "publisher": { "name": "magazine.sebastianraschka.com", "url": "https://magazine.sebastianraschka.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-fd95ec29524d.webp", "width": 1200, "height": 672, "sourcePage": "https://magazine.sebastianraschka.com/p/practical-tips-for-finetuning-llms", "sourceImage": "https://substackcdn.com/image/fetch/$s_!i3QH!,w_1200,h_675,c_fill,f_jpg,q_auto:good,fl_progressive:steep,g_auto/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5dfbd169-eb7e-41e1-a050-556ccd6fb679_1600x672.png", "sourceWidth": 1200, "sourceHeight": 672, "credit": "magazine.sebastianraschka.com", "thumbnail": { "path": "/img/library/ref-fd95ec29524d-small.webp", "width": 480, "height": 269 } } }, "summary": "Practical observations from fine-tuning experiments with LoRA.", "curation": { "note": null, "listingNote": "Practical observations from fine-tuning experiments with LoRA." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Model adaptation", "Observability" ], "related": [ { "id": "lora-low-rank-adaptation-of-large-language-models", "reason": "The original LoRA paper explains the method behind the practical experiments." }, { "id": "easily-train-a-specialized-llm-peft-lora-qlora-llama-adapter-and-more", "reason": "A broader guide compares LoRA with other parameter-efficient adaptation techniques." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/practical-tips-for-finetuning-llms-using-lora-low-rank-adaptation", "status": "curated" }, { "id": "makefile-tutorial-by-example", "slug": "makefile-tutorial-by-example", "source": { "title": "Makefile Tutorial by Example", "type": "website", "canonicalUrl": "https://makefiletutorial.com/", "authors": [], "publisher": { "name": "Makefile Tutorial", "url": "https://makefiletutorial.com" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "sourcePage": "https://makefiletutorial.com/", "sourceImage": "https://makefiletutorial.com/assets/dependency_graph.png", 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management of cached attention states." }, { "id": "calculating-gpu-memory-for-serving-llms", "reason": "GPU-memory estimates put the cache’s resource demands into deployment context." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/transformers-kv-caching-explained", "status": "curated" }, { "id": "openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddings", "slug": "openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddings", "source": { "title": "OpenAI GPT-3 Text Embeddings - Really a new state-of-the-art in dense text embeddings?", "type": "article", "canonicalUrl": "https://medium.com/@nils_reimers/openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddings-6571fe3ec9d9", "authors": [ { "name": "Nils Reimers", "type": "Person" } ], "publisher": { "name": "Medium", "url": "https://medium.com" }, "datePublished": "2022-02-09", "access": "restricted", "schemaType": "Article", 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"public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://github.com/milvus-io/milvus-docs/blob/v2.6.x/site/en/about/overview.md", "sourceImage": "https://raw.githubusercontent.com/milvus-io/milvus-docs/v2.6.x/assets/unstructured-data-embedding-and-milvus.png", "credit": "Milvus", "kind": "source-image", "alt": "Diagram showing unstructured data transformed into embeddings for storage and search in Milvus.", "sourceWidth": 2280, "sourceHeight": 1314, "path": "/img/library/what-is-milvus.webp", "width": 1200, "height": 692, "thumbnail": { "path": "/img/library/what-is-milvus-small.webp", "width": 480, "height": 277 } } }, "summary": "An introduction to Milvus and its architecture for storing and searching vector data.", "curation": { "note": null, "listingNote": "An introduction to Milvus and its architecture for storing and searching vector data." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval" ], "related": [ { "id": "vector-database-management-systems-fundamental-concepts-use-cases-and-current-challen", "reason": "The vector-database survey provides criteria for comparing Milvus with other architectures." }, { "id": "pgvector-pgvector", "reason": "pgvector offers a contrasting design built on PostgreSQL." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/what-is-milvus", "status": "curated" }, { "id": "fine-tune-mistral-7b-with-direct-preference-optimization", "slug": "fine-tune-mistral-7b-with-direct-preference-optimization", "source": { "title": "Fine-tune Mistral-7b with Direct Preference Optimization", "type": "article", "canonicalUrl": "https://mlabonne.github.io/blog/posts/Fine_tune_Mistral_7b_with_DPO.html", "authors": [ { "name": "Maxime Labonne", "type": "Person" } ], "publisher": { "name": "Maxime Labonne", "url": "https://mlabonne.github.io" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://mlabonne.github.io/blog/posts/Fine_tune_Mistral_7b_with_DPO.html", "credit": "Maxime Labonne", "kind": "source-image", "alt": "Original article illustration of a classical head with a colorful electronic brain", "sourceWidth": 1792, "sourceHeight": 1024, "path": "/img/library/fine-tune-mistral-7b-with-direct-preference-optimization.webp", "width": 1200, "height": 686, "thumbnail": { "path": "/img/library/fine-tune-mistral-7b-with-direct-preference-optimization-small.webp", "width": 480, "height": 274 }, "caption": "Original illustration embedded in the article." } }, "summary": "A worked guide to fine-tuning Mistral 7B with direct preference optimization.", "curation": { "note": null, "listingNote": "A worked guide to fine-tuning Mistral 7B with direct preference optimization." }, "topics": [ "AI & machine learning" ], "tags": [ "Model adaptation" ], "related": [ { "id": "direct-preference-optimization-your-language-model-is-secretly-a-reward-model", "reason": "The DPO paper explains the method used in this fine-tuning walkthrough." }, { "id": "dpo-trainer", "reason": "The trainer documentation provides an implementation reference." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/fine-tune-mistral-7b-with-direct-preference-optimization", "status": "curated" }, { "id": "quantize-llama-models-with-gguf-and-llama-cpp", "slug": "quantize-llama-models-with-gguf-and-llama-cpp", "source": { "title": "Quantize Llama models with GGUF and llama.cpp", "type": "article", "canonicalUrl": "https://mlabonne.github.io/blog/posts/Quantize_Llama_2_models_using_ggml.html", "authors": [ { "name": "Maxime Labonne", "type": "Person" } ], "publisher": { "name": "Maxime Labonne", "url": "https://mlabonne.github.io" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://mlabonne.github.io/blog/posts/Quantize_Llama_2_models_using_ggml.html", "credit": "Maxime Labonne", "kind": "source-image", "alt": "Original article illustration of a llama using a desktop computer", "sourceWidth": 1920, "sourceHeight": 1080, "path": "/img/library/quantize-llama-models-with-gguf-and-llama-cpp.webp", "width": 1200, "height": 675, "thumbnail": { "path": "/img/library/quantize-llama-models-with-gguf-and-llama-cpp-small.webp", "width": 480, "height": 270 }, "caption": "Original illustration embedded in the article." } }, "summary": "A guide to quantizing models for use with llama.cpp.", "curation": { "note": null, "listingNote": "A guide to quantizing models for use with llama.cpp." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Model adaptation", "Inference" ], "related": [ { "id": "k-quants-by-ikawrakow-pull-request-1684-ggml-org-llama-cpp", "reason": "The k-quants pull request documents part of the quantization implementation." }, { "id": "thebloke-codellama-7b-gguf", "reason": "A concrete quantized Code Llama distribution shows the GGUF format in use." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/quantize-llama-models-with-gguf-and-llama-cpp", "status": "curated" }, { "id": "louvain-communities", "slug": "louvain-communities", "source": { "title": "louvain_communities", "type": "documentation", "canonicalUrl": "https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.community.louvain.louvain_communities.html", "authors": [], "publisher": { "name": "networkx.org", "url": "https://networkx.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.community.louvain.louvain_communities.html", "credit": "networkx.org", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "NetworkX reference for Louvain community detection, including its modularity equation.", "sourceWidth": 752, "sourceHeight": 800, "path": "/img/library/louvain-communities.webp", "width": 752, "height": 800, "thumbnail": { "path": "/img/library/louvain-communities-small.webp", "width": 480, "height": 511 } } }, "summary": "NetworkX documentation for Louvain community detection.", "curation": { "note": null, "listingNote": "NetworkX documentation for Louvain community detection." }, "topics": [ "Engineering" ], "tags": [ "Knowledge graphs" ], "related": [ { "id": "leiden-clustering-for-community-detection-a-step-by-step-guide-with-python-implementa", "reason": "Compare Louvain with another community-detection algorithm and its Python workflow." }, { "id": "betweenness-centrality", "reason": "Centrality measures offer a different way to examine graph structure." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/louvain-communities", "status": "curated" }, { "id": "pagerank", "slug": "pagerank", "source": { "title": "pagerank", "type": "documentation", "canonicalUrl": "https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.link_analysis.pagerank_alg.pagerank.html", "authors": [], "publisher": { "name": "networkx.org", "url": "https://networkx.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.link_analysis.pagerank_alg.pagerank.html", "credit": "networkx.org", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "NetworkX PageRank reference showing function arguments and the algorithm description.", "sourceWidth": 752, "sourceHeight": 800, "path": "/img/library/pagerank.webp", "width": 752, "height": 800, "thumbnail": { "path": "/img/library/pagerank-small.webp", "width": 480, "height": 511 } } }, "summary": "NetworkX documentation for computing PageRank.", "curation": { "note": null, "listingNote": "NetworkX documentation for computing PageRank." }, "topics": [ "Engineering" ], "tags": [ "Knowledge graphs" ], "related": [ { "id": "betweenness-centrality", "reason": "Compare link-based importance with betweenness centrality." }, { "id": "introduction-information-retrieval", "reason": "The retrieval textbook places graph-based importance within web search." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/pagerank", "status": "curated" }, { "id": "openllm-observability-integration", "slug": "openllm-observability-integration", "source": { "title": "OpenLLM observability integration", "type": "website", "canonicalUrl": "https://newrelic.com/instant-observability/openllm", "authors": [], "publisher": { "name": "New Relic", "url": "https://newrelic.com" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-f5f44a106216.webp", "width": 1200, "height": 676, "sourcePage": "https://newrelic.com/instant-observability/openllm", "sourceImage": "https://newrelic.com/themes/custom/erno/assets/images/metadata/NROG_Image.png", "sourceWidth": 2400, "sourceHeight": 1352, "credit": "New Relic", "thumbnail": { "path": "/img/library/ref-f5f44a106216-small.webp", "width": 480, "height": 270 } } }, "summary": "An observability integration for monitoring OpenLLM.", "curation": { "note": null, "listingNote": "An observability integration for monitoring OpenLLM." }, "topics": [ "Engineering" ], "tags": [ "Observability" ], "related": [ { "id": "best-practices-for-llm-inference-performance-monitoring", "reason": "Monitoring guidance helps interpret the operational metrics exposed by a model integration." }, { "id": "opentelemetry", "reason": "OpenTelemetry supplies related observability concepts and instrumentation standards." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/openllm-observability-integration", "status": "curated" }, { "id": "introducing-deep-research", "slug": "introducing-deep-research", "source": { "title": "Introducing deep research", "type": "article", "canonicalUrl": "https://openai.com/index/introducing-deep-research/", "authors": [], "publisher": { "name": "OpenAI", "url": "https://openai.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-73bd5375d8a8.webp", "width": 1200, "height": 675, "sourcePage": "https://openai.com/index/introducing-deep-research/", "sourceImage": "https://images.ctfassets.net/kftzwdyauwt9/3vFGiTldrRNI3dHPz2yJqg/4b5a8ceed80b19ec2fec4ebccae64518/OAI_deep_research_16.9.png?w=1600&h=900&fit=fill", "sourceWidth": 1600, "sourceHeight": 900, "credit": "OpenAI", "thumbnail": { "path": "/img/library/ref-73bd5375d8a8-small.webp", "width": 480, "height": 270 } } }, "summary": "OpenAI’s introduction to its research-oriented agent.", "curation": { "note": null, "listingNote": "OpenAI’s introduction to its research-oriented agent." }, "topics": [ "AI & machine learning" ], "tags": [ "Agents" ], "related": [ { "id": "deepsearch-deepresearch", "reason": "An implementation guide examines the search, reading, and reasoning loop behind research agents." }, { "id": "surveyx-academic-survey-automation-via-large-language-models", "reason": "Survey automation is another application of iterative research and synthesis." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/introducing-deep-research", "status": "curated" }, { "id": "european-open-source-ai-index", "slug": "european-open-source-ai-index", "source": { "title": "European Open Source AI Index", "type": "website", "canonicalUrl": "https://osai-index.eu/", "authors": [], "publisher": { "name": "European Open Source AI Index", "url": "https://osai-index.eu" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "sourcePage": "https://osai-index.eu/", "credit": "European Open Source AI Index", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "European Open Source AI Index website introducing its assessment of open generative AI systems.", "sourceWidth": 1200, "sourceHeight": 850, "path": "/img/library/european-open-source-ai-index.webp", "width": 1200, "height": 850, "thumbnail": { "path": "/img/library/european-open-source-ai-index-small.webp", "width": 480, "height": 340 } } }, "summary": "An index assessing the openness of AI systems.", "curation": { "note": null, "listingNote": "An index assessing the openness of AI systems." }, "topics": [ "Engineering" ], "tags": [ "Open source" ], "related": [ { "id": "opening-up-chatgpt", "reason": "The earlier openness project points readers toward this successor index." }, { "id": "opening-up-chatgpt-tracking-openness-transparency-and-accountability-in-instruction-t", "reason": "The research paper provides background on assessing model openness and accountability." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/european-open-source-ai-index", "status": "curated", "citedIn": [ { "title": "LLM : souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "diskann-fast-accurate-billion-point-nearest-neighbor-search-on-a-single-node", "slug": "diskann-fast-accurate-billion-point-nearest-neighbor-search-on-a-single-node", "source": { "title": "DiskANN: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node", "type": "paper", "canonicalUrl": "https://papers.nips.cc/paper_files/paper/2019/hash/09853c7fb1d3f8ee67a61b6bf4a7f8e6-Abstract.html", "authors": [ { "name": "Suhas Jayaram Subramanya", "type": "Person" }, { "name": "Devvrit", "type": "Person" }, { "name": "Harsha Vardhan Simhadri", "type": "Person" }, { "name": "Ravishankar Krishnaswamy", "type": "Person" }, { "name": "Rohan Kadekodi", "type": "Person" } ], "publisher": { "name": "papers.nips.cc", "url": "https://papers.nips.cc" }, "datePublished": null, "access": "public", "schemaType": "ScholarlyArticle", "pdfUrl": "https://papers.nips.cc/paper_files/paper/2019/file/09853c7fb1d3f8ee67a61b6bf4a7f8e6-Paper.pdf", "preview": { "sourcePage": "https://papers.nips.cc/paper_files/paper/2019/hash/09853c7fb1d3f8ee67a61b6bf4a7f8e6-Abstract.html", "sourceImage": "https://papers.nips.cc/paper_files/paper/2019/file/09853c7fb1d3f8ee67a61b6bf4a7f8e6-Paper.pdf", "credit": "Suhas Jayaram Subramanya, Devvrit, Harsha Vardhan Simhadri, Ravishankar Krishnaswamy, Rohan Kadekodi", "kind": "paper-first-page", "alt": "Title page of DiskANN: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node.", "sourceWidth": 1391, "sourceHeight": 1800, "path": "/img/library/diskann-fast-accurate-billion-point-nearest-neighbor-search-on-a-single-node.webp", "width": 1200, "height": 1553, "thumbnail": { "path": "/img/library/diskann-fast-accurate-billion-point-nearest-neighbor-search-on-a-single-node-small.webp", "width": 480, "height": 621 } } }, "summary": "A method for billion-point approximate nearest-neighbor search on one machine.", "curation": { "note": null, "listingNote": "A method for billion-point approximate nearest-neighbor search on one machine." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "evaluation-of-approximate-nearest-neighbors-large-datasets", "reason": "Large vector datasets provide the evaluation setting for billion-point search." }, { "id": "searching-in-one-billion-vectors-re-rank-with-source-coding", "reason": "Compare another approach to nearest-neighbor search at billion-vector scale." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/diskann-fast-accurate-billion-point-nearest-neighbor-search-on-a-single-node", "status": "curated" }, { "id": "pre-commit", "slug": "pre-commit", "source": { "title": "pre-commit", "type": "website", "canonicalUrl": "https://pre-commit.com/", "authors": [], "publisher": { "name": "pre-commit.com", "url": "https://pre-commit.com" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "sourcePage": "https://pre-commit.com/", "credit": "pre-commit.com", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "pre-commit project page introducing its hook framework and installation workflow.", "sourceWidth": 1200, "sourceHeight": 850, "path": "/img/library/pre-commit.webp", "width": 1200, "height": 850, "thumbnail": { "path": "/img/library/pre-commit-small.webp", "width": 480, "height": 340 } } }, "summary": "A framework for managing and sharing pre-commit hooks.", "curation": { "note": null, "listingNote": "A framework for managing and sharing pre-commit hooks." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "biome", "reason": "Formatting and linting are examples of checks that can fit into a pre-commit workflow." }, { "id": "declutter-your-javascript-typescript-projects", "reason": "Unused-code detection complements other automated project checks." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/pre-commit", "status": "curated" }, { "id": "prometheus-monitoring-system-time-series-database", "slug": "prometheus-monitoring-system-time-series-database", "source": { "title": "Prometheus - Monitoring system & time series database", "type": "website", "canonicalUrl": "https://prometheus.io/", "authors": [], "publisher": { "name": "prometheus.io", "url": "https://prometheus.io" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-6f32c58848af.webp", "width": 1200, "height": 1200, "sourcePage": "https://prometheus.io/", "sourceImage": "https://prometheus.io/opengraph-image.png?b370f6418ef38b42", "sourceWidth": 1200, "sourceHeight": 1200, "credit": "prometheus.io", "thumbnail": { "path": "/img/library/ref-6f32c58848af-small.webp", "width": 480, "height": 480 } } }, "summary": "An open monitoring system and time-series database.", "curation": { "note": null, "listingNote": "An open monitoring system and time-series database." }, "topics": [ "Engineering" ], "tags": [ "Observability" ], "related": [ { "id": "prometheus-and-grafana", "reason": "The example shows Prometheus collecting metrics from a vLLM server." }, { "id": "full-stack-observability-for-the-agentic-era-grafana-labs", "reason": "Grafana provides a complementary interface for exploring monitoring data." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/prometheus-monitoring-system-time-series-database", "status": "curated" }, { "id": "filterable-hnsw-without-recall-loss", "slug": "filterable-hnsw-without-recall-loss", "source": { "title": "Filterable HNSW Without Recall Loss", "type": "article", "canonicalUrl": "https://qdrant.tech/articles/filterable-hnsw/", "authors": [ { "name": "Andrei Vasnetsov", "type": "Person" } ], "publisher": { "name": "qdrant.tech", "url": "https://qdrant.tech" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://qdrant.tech/articles/filterable-hnsw/", "credit": "qdrant.tech", "kind": "source-image", "alt": "Original Qdrant article illustration of a glowing network", "sourceWidth": 898, "sourceHeight": 300, "path": "/img/library/filterable-hnsw-without-recall-loss.webp", "width": 898, "height": 300, "thumbnail": { "path": "/img/library/filterable-hnsw-without-recall-loss-small.webp", "width": 480, "height": 160 }, "sourceImage": "https://qdrant.tech/articles_data/filterable-hnsw/preview/title.jpg" } }, "summary": "An explanation of filtered vector search using HNSW.", "curation": { "note": null, "listingNote": "An explanation of filtered vector search using HNSW." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval" ], "related": [ { "id": "efficient-and-robust-approximate-nearest-neighbor-search-using-hierarchical-navigable", "reason": "The original HNSW paper explains the underlying graph index." }, { "id": "filtering", "reason": "Qdrant’s filter documentation shows how constraints are expressed in searches." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/filterable-hnsw-without-recall-loss", "status": "curated" }, { "id": "filtering", "slug": "filtering", "source": { "title": "Filtering", "type": "documentation", "canonicalUrl": "https://qdrant.tech/documentation/search/filtering/", "authors": [], "publisher": { "name": "qdrant.tech", "url": "https://qdrant.tech" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://qdrant.tech/documentation/search/filtering/", "credit": "qdrant.tech", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "Qdrant filtering guide showing filter clauses and a JSON example.", "sourceWidth": 844, "sourceHeight": 800, "path": "/img/library/filtering.webp", "width": 844, "height": 800, "thumbnail": { "path": "/img/library/filtering-small.webp", "width": 480, "height": 455 } } }, "summary": "Qdrant documentation for constraining search results with filters.", "curation": { "note": null, "listingNote": "Qdrant documentation for constraining search results with filters." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval" ], "related": [ { "id": "filterable-hnsw-without-recall-loss", "reason": "The filtered-HNSW article explains index behavior when search constraints are applied." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/filtering", "status": "curated" }, { "id": "swiss-cheese-model-safeche-process-safety", "slug": "swiss-cheese-model-safeche-process-safety", "source": { "title": "Swiss Cheese Model – SAFEChE: Process Safety", "type": "documentation", "canonicalUrl": "https://safeche.engin.umich.edu/tutorials/swiss-cheese-model/", "authors": [], "publisher": { "name": "safeche.engin.umich.edu", "url": "https://safeche.engin.umich.edu" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://web.archive.org/web/20250811045745/https://safeche.engin.umich.edu/tutorials/swiss-cheese-model/", "credit": "safeche.engin.umich.edu / Internet Archive", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "SAFEChE explanation and diagram of layered protective barriers in the Swiss cheese model.", "sourceWidth": 832, "sourceHeight": 900, "path": "/img/library/swiss-cheese-model-safeche-process-safety.webp", "width": 832, "height": 900, "thumbnail": { "path": "/img/library/swiss-cheese-model-safeche-process-safety-small.webp", "width": 480, "height": 519 } } }, "summary": "An explanation of layered defenses through the Swiss cheese model.", "curation": { "note": null, "listingNote": "An explanation of layered defenses through the Swiss cheese model." }, "topics": [ "Security & defense" ], "tags": [ "Security" ], "related": [ { "id": "autonomy-in-weapon-systems-dod-directive-3000-09", "reason": "The autonomy directive offers a concrete 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"https://sigir.org/afirm2019/slides/16.%20Friday%20-%20Music%20Recommendation%20at%20Spotify%20-%20Ben%20Carterette.pdf", "authors": [ { "name": "Ben Carterette", "type": "Person" } ], "publisher": { "name": "SIGIR AFIRM", "url": "https://sigir.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "publicationYear": 2019, "preview": { "sourcePage": "https://sigir.org/afirm2019/slides/16.%20Friday%20-%20Music%20Recommendation%20at%20Spotify%20-%20Ben%20Carterette.pdf", "sourceImage": "https://sigir.org/afirm2019/slides/16.%20Friday%20-%20Music%20Recommendation%20at%20Spotify%20-%20Ben%20Carterette.pdf", "credit": "Ben Carterette", "kind": "document-page", "alt": "First page of Music recommendation at Spotify.", "sourceWidth": 1800, "sourceHeight": 1013, "path": "/img/library/music-recommendation-at-spotify.webp", "width": 1200, "height": 675, "thumbnail": { "path": "/img/library/music-recommendation-at-spotify-small.webp", "width": 480, "height": 270 }, "pageNumber": 1 } }, "summary": "A presentation about the research and systems behind music recommendations at Spotify.", "curation": { "note": null, "listingNote": "A presentation about the research and systems behind music recommendations at Spotify." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "carousel-personalization-in-music-streaming-apps-with-contextual-bandits", "reason": "A focused paper examines carousel personalization in music-streaming applications." }, { "id": "a-framework-for-offline-evaluation-of-recommender-systems-based-on-probabilistic-rela", "reason": "An offline-evaluation framework complements the presentation of recommender systems." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/music-recommendation-at-spotify", "status": "curated" }, { "id": "cost-optimized-hosting-of-fine-tuned-llms-in-production-microsoft-community-hub", "slug": "cost-optimized-hosting-of-fine-tuned-llms-in-production-microsoft-community-hub", "source": { "title": "Cost Optimized hosting of Fine-tuned LLMs in Production | Microsoft Community Hub", "type": "article", "canonicalUrl": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/cost-optimized-hosting-of-fine-tuned-llms-in-production/4062192", "authors": [], "publisher": { "name": "TECHCOMMUNITY.MICROSOFT.COM", "url": "https://techcommunity.microsoft.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-0336f22db9bf.webp", "width": 1200, "height": 637, "sourcePage": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/cost-optimized-hosting-of-fine-tuned-llms-in-production/4062192", "sourceImage": "https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MDYyMTkyLTU1NDQ5NmlDNDFGMkNBREM1M0FEQTkw?revision=29", "sourceWidth": 1370, "sourceHeight": 727, "credit": "TECHCOMMUNITY.MICROSOFT.COM", "thumbnail": { "path": "/img/library/ref-0336f22db9bf-small.webp", "width": 480, "height": 255 } } }, "summary": "A deployment pattern for reducing the cost of serving fine-tuned models.", "curation": { "note": null, "listingNote": "A deployment pattern for reducing the cost of serving fine-tuned models." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Model adaptation", "Inference", "Economics", "Product engineering" ], "related": [ { "id": "llm-inference-performance", "reason": "Inference-performance engineering provides the technical basis for reducing serving cost." }, { "id": "llm-economics-which-is-cheaper-to-deploy-chatgpt-vs-open-source-llms", "reason": "Compare deployment optimizations with the broader hosted-versus-self-hosted cost decision." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/cost-optimized-hosting-of-fine-tuned-llms-in-production-microsoft-community-hub", "status": "curated" }, { "id": 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"curation": { "note": null, "listingNote": "A discussion of the metrics and frameworks available for evaluating retrieval-augmented generation." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Retrieval", "Evaluation", "Observability" ], "related": [ { "id": "ragas-automated-evaluation-of-retrieval-augmented-generation", "reason": "The Ragas paper describes one framework discussed in RAG evaluation." }, { "id": "the-rag-triad", "reason": "The RAG triad provides a compact comparison of the main quality dimensions." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/rag-evaluation-metrics-and-frameworks", "status": "curated" }, { "id": "how-to-leverage-large-language-models-without-breaking-the-bank", "slug": "how-to-leverage-large-language-models-without-breaking-the-bank", "source": { "title": "How to leverage large language models without breaking the bank", "type": "article", "canonicalUrl": 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"https://images.ctfassets.net/jdtwqhzvc2n1/6stYPv77lHFRGXcceKyvqL/aed45f45d23f42df942a51ea70e47a8c/annevb_business_artificial_intelligence_and_money._Colorful_fu_7471d12c-f87c-4ea8-a32f-09423380fa0d.png?w=1000&q=100" } }, "summary": "Inference costs and open models.", "curation": { "note": null, "listingNote": "Inference costs and open models." }, "topics": [ "Engineering", "Company building" ], "tags": [ "Inference", "Economics" ], "related": [ { "id": "llm-economics-which-is-cheaper-to-deploy-chatgpt-vs-open-source-llms", "reason": "Compare a detailed hosted-versus-open-model cost analysis." }, { "id": "llm-inference-performance", "reason": "Performance engineering explains some of the levers behind inference cost." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/how-to-leverage-large-language-models-without-breaking-the-bank", "status": "curated" }, { "id": "how-tsmc-surpassed-intel-in-advanced-chip-technology", "slug": "how-tsmc-surpassed-intel-in-advanced-chip-technology", "source": { "title": "How TSMC Surpassed Intel in Advanced Chip Technology", "type": "article", "canonicalUrl": "https://vestedfinance.com/in/blog/the-empires-that-tsmc-built/", "authors": [ { "name": "Parth Parikh", "type": "Person" } ], "publisher": { "name": "Vested Finance", "url": "https://vestedfinance.com" }, "datePublished": "2022-03-28", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-9bffd7531c2b.webp", "width": 669, "height": 224, "sourcePage": "https://vestedfinance.com/in/blog/the-empires-that-tsmc-built/", "sourceImage": "https://vestedfinance.com/wp-content/uploads/2022/03/upload-1.jpg", "sourceWidth": 669, "sourceHeight": 224, "credit": "Vested Finance", "thumbnail": { "path": "/img/library/ref-9bffd7531c2b-small.webp", "width": 480, "height": 161 } } }, "summary": "An account of TSMC’s rise in advanced chip manufacturing.", "curation": { "note": null, "listingNote": "An account of TSMC’s rise in advanced chip manufacturing." }, "topics": [ "Engineering" ], "tags": [ "Semiconductors" ], "related": [ { "id": "reverse-engineering-the-semiconductor-industry", "reason": "The industry overview explains manufacturing models and supply-chain dependencies." }, { "id": "chip-war", "reason": "Chip War supplies a longer history of semiconductor competition." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/how-tsmc-surpassed-intel-in-advanced-chip-technology", "status": "curated", "citedIn": [ { "title": "LLM : souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "fact-sheet-chips-and-science-act-will-lower-costs-create-jobs-strengthen-supply-chain", "slug": "fact-sheet-chips-and-science-act-will-lower-costs-create-jobs-strengthen-supply-chain", "source": { "title": "FACT SHEET: CHIPS and Science Act Will Lower Costs, Create Jobs, Strengthen Supply Chains, and Counter China | The White House", "type": "article", "canonicalUrl": "https://www.whitehouse.gov/briefing-room/statements-releases/2022/08/09/fact-sheet-chips-and-science-act-will-lower-costs-create-jobs-strengthen-supply-chains-and-counter-china/", "authors": [ { "name": "The White House", "type": "Organization" } ], "publisher": { "name": "The White House", "url": "https://www.whitehouse.gov" }, "datePublished": "2022-08-09", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-998aaff98abd.webp", "width": 1200, "height": 630, "sourcePage": "https://web.archive.org/web/20250118011306/https://www.whitehouse.gov/briefing-room/statements-releases/2022/08/09/fact-sheet-chips-and-science-act-will-lower-costs-create-jobs-strengthen-supply-chains-and-counter-china/", "sourceImage": "https://web.archive.org/web/20250118011306im_/https://www.whitehouse.gov/wp-content/uploads/2021/01/wh_social-share.png", "sourceWidth": 1200, "sourceHeight": 630, "credit": "The White House", "thumbnail": { "path": "/img/library/ref-998aaff98abd-small.webp", "width": 480, "height": 252 } } }, "summary": "The White House fact sheet introducing the CHIPS and Science Act.", "curation": { "note": null, "listingNote": "The White House fact sheet introducing the CHIPS and Science Act." }, "topics": [ "Company building", "Engineering" ], "tags": [ "Economics", "Semiconductors" ], "related": [ { "id": "european-chips-act", "reason": "Compare US semiconductor policy with the European Chips Act." }, { "id": "semiconductor-resilience", "reason": "The supply-chain analysis places industrial policy within global manufacturing dependencies." } ], "archive": { "snapshotUrl": "https://web.archive.org/web/20250118011306/https://www.whitehouse.gov/briefing-room/statements-releases/2022/08/09/fact-sheet-chips-and-science-act-will-lower-costs-create-jobs-strengthen-supply-chains-and-counter-china/" }, "detailUrl": "https://fpaupier.fr/library/fact-sheet-chips-and-science-act-will-lower-costs-create-jobs-strengthen-supply-chain", "status": "curated", "citedIn": [ { "title": "LLM : souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "speech-and-language-processing-rnns-and-lstms", "slug": "speech-and-language-processing-rnns-and-lstms", "source": { "title": "Speech and Language Processing: RNNs and LSTMs", "type": "book", "canonicalUrl": "https://web.stanford.edu/~jurafsky/slp3/14.pdf", "authors": [ { "name": "Daniel Jurafsky", "type": "Person" }, { "name": "James H. 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"/img/library/parameter-efficient-tuning.webp", "width": 1200, "height": 675, "thumbnail": { "path": "/img/library/parameter-efficient-tuning-small.webp", "width": 480, "height": 270 }, "pageNumber": 1 } }, "summary": "Course slides introducing methods that adapt language models with relatively few trainable parameters.", "curation": { "note": null, "listingNote": "Course slides introducing methods that adapt language models with relatively few trainable parameters." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "lora-low-rank-adaptation-of-large-language-models", "reason": "LoRA is a specific method for adapting models through low-rank updates." }, { "id": "easily-train-a-specialized-llm-peft-lora-qlora-llama-adapter-and-more", "reason": "A practical guide compares several parameter-efficient adaptation techniques." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/parameter-efficient-tuning", "status": "curated" }, { "id": "asml-euv-lithography-systems", "slug": "asml-euv-lithography-systems", "source": { "title": "ASML EUV lithography systems", "type": "website", "canonicalUrl": "https://www.asml.com/en/products/euv-lithography-systems", "authors": [], "publisher": { "name": "ASML", "url": "https://www.asml.com" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-0701df86d08f.webp", "width": 1200, "height": 675, "sourcePage": "https://www.asml.com/en/products/euv-lithography-systems", "sourceImage": "https://media.asml.com/asmlnetherlaaea-asmlcom-prd-5369/media/project/asmlcom/asmlcom/asml/images/products/euv-lithography-systems/nxe3400_simplified_front_semiclosed.jpg?h=1080&iar=0&w=1920", "sourceWidth": 1920, "sourceHeight": 1080, "credit": "ASML", "thumbnail": { "path": "/img/library/ref-0701df86d08f-small.webp", "width": 480, "height": 270 } } }, "summary": "ASML’s explanation of extreme-ultraviolet lithography systems.", "curation": { "note": null, "listingNote": "ASML’s explanation of extreme-ultraviolet lithography systems." }, "topics": [ "Engineering" ], "tags": [ "Knowledge graphs", "Semiconductors" ], "related": [ { "id": "how-are-microchips-made-cpu-manufacturing-process-steps", "reason": "A chip-manufacturing overview places EUV lithography within the production process." }, { "id": "reverse-engineering-the-semiconductor-industry", "reason": "The industry overview explains why manufacturing tools create supply-chain dependencies." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/asml-euv-lithography-systems", "status": "curated", "citedIn": [ { "title": "LLM : souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "what-in-the-world-is-finops-and-why-do-we-need-it-inside-atlassian", "slug": "what-in-the-world-is-finops-and-why-do-we-need-it-inside-atlassian", "source": { "title": "What in the world is FinOps, and why do we need it? - Inside Atlassian", "type": "article", "canonicalUrl": "https://www.atlassian.com/blog/innovation/what-is-finops", "authors": [ { "name": "Gigi Griffis", "type": "Person" } ], "publisher": { "name": "Inside Atlassian", "url": "https://www.atlassian.com" }, "datePublished": "2021-04-12", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-cf2dbdb581f7.webp", "width": 1200, "height": 641, "sourcePage": "https://www.atlassian.com/blog/innovation/what-is-finops", "sourceImage": "https://atlassianblog.wpengine.com/wp-content/uploads/2021/04/artboard-1@2x.png", "sourceWidth": 2320, "sourceHeight": 1240, "credit": "Inside Atlassian", "thumbnail": { "path": "/img/library/ref-cf2dbdb581f7-small.webp", "width": 480, "height": 256 } } }, "summary": "An introduction to FinOps and the management of cloud expenditure.", "curation": { "note": null, "listingNote": "An introduction to FinOps and the management of cloud expenditure." }, "topics": [ "Company building" ], "tags": [ "Economics" ], "related": [ { "id": "how-much-does-ai-cost-in-2025-well-it-depends", "reason": "AI development costs provide a concrete setting for cloud-spending management." }, { "id": "the-economics-of-llms", "reason": "Language-model economics adds detail on deployment and model-selection costs." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/what-in-the-world-is-finops-and-why-do-we-need-it-inside-atlassian", "status": "curated" }, { "id": "genai-needs-pricing-strategies-to-match-its-potential", "slug": "genai-needs-pricing-strategies-to-match-its-potential", "source": { "title": "GenAI Needs Pricing Strategies to Match Its Potential", "type": "article", "canonicalUrl": "https://www.bcg.com/publications/2024/genai-needs-pricing-strategies-to-match-its-potential", "authors": [ { "name": "John Pineda", "type": "Person" }, { "name": "Jacob Konikoff", "type": "Person" }, { "name": "Saran Rajendran", "type": "Person" }, { "name": "Pranay Ahlawat", "type": "Person" }, { "name": "Jean-Manuel Izaret", "type": "Person" } ], "publisher": { "name": "Boston Consulting Group", "url": "https://www.bcg.com" }, "datePublished": "2024-02-12", "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://www.bcg.com/publications/2024/genai-needs-pricing-strategies-to-match-its-potential", "credit": "Boston Consulting Group", "kind": "source-image", "alt": "BCG table comparing consumption, user-based, and outcome-based generative AI pricing", "sourceWidth": 2500, "sourceHeight": 1640, "path": "/img/library/genai-needs-pricing-strategies-to-match-its-potential.webp", "width": 1200, "height": 787, "thumbnail": { "path": "/img/library/genai-needs-pricing-strategies-to-match-its-potential-small.webp", "width": 480, "height": 315 }, "sourceImage": "https://boston-consulting-group-brightspot.s3.amazonaws.com/5b/e3/4a5143384148aa0eb2e500553b80/generative-ai-needs-pricing-strategies-to-match-its-potential-ex02.png" } }, "summary": "A pricing framework connecting customer value, differentiation, and the changing costs of generative AI.", "curation": { "note": null, "listingNote": "A pricing framework connecting customer value, differentiation, and the changing costs of generative AI." }, "topics": [ "Company building" ], "tags": [ "Economics" ], "related": [ { "id": "sales-playbook", "reason": "Founder sales practices provide context for discussing value and pricing with customers." }, { "id": "what-is-a-llm-pricing-strategy", "reason": "Compare another account of pricing approaches for language-model services." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/genai-needs-pricing-strategies-to-match-its-potential", "status": "curated" }, { "id": "7-ways-to-deploy-your-own-large-language-model", "slug": "7-ways-to-deploy-your-own-large-language-model", "source": { "title": "7 ways to deploy your own large language model", "type": "article", "canonicalUrl": "https://www.cio.com/article/1224909/5-ways-to-deploy-your-own-large-language-model.html?amp=1", "authors": [], "publisher": { "name": "CIO", "url": "https://www.cio.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-72a2157e7c80.webp", "width": 1024, "height": 682, "sourcePage": "https://www.cio.com/article/1224909/5-ways-to-deploy-your-own-large-language-model.html?amp=1", "sourceImage": "https://www.cio.com/wp-content/uploads/2025/05/1224909-0-87211400-1747893656-iStock-1035518406.jpg?quality=50&strip=all&w=1024", "sourceWidth": 1024, "sourceHeight": 682, "credit": "CIO", "thumbnail": { "path": "/img/library/ref-72a2157e7c80-small.webp", "width": 480, "height": 320 } } }, "summary": "A comparison of approaches to deploying language models.", "curation": { "note": null, "listingNote": "A comparison of approaches to deploying language models." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "text-generation-inference", "reason": "TGI provides a concrete implementation of a model-serving approach." }, { "id": "vllm-project-vllm", "reason": "vLLM is another serving engine to compare when choosing a deployment strategy." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/7-ways-to-deploy-your-own-large-language-model", "status": "curated" }, { "id": "chatgpt-and-generative-ai-are-booming-but-the-costs-can-be-extraordinary", "slug": "chatgpt-and-generative-ai-are-booming-but-the-costs-can-be-extraordinary", "source": { "title": "ChatGPT and generative AI are booming, but the costs can be extraordinary", "type": "article", "canonicalUrl": "https://www.cnbc.com/2023/03/13/chatgpt-and-generative-ai-are-booming-but-at-a-very-expensive-price.html", "authors": [ { "name": "Jonathan Vanian", "type": "Person" }, { "name": "Kif Leswing", "type": "Person" } ], "publisher": { "name": "CNBC", "url": "https://www.cnbc.com" }, "datePublished": "2023-03-13", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-e2b21038014e.webp", "width": 1200, "height": 675, "sourcePage": "https://www.cnbc.com/2023/03/13/chatgpt-and-generative-ai-are-booming-but-at-a-very-expensive-price.html", "sourceImage": "https://image.cnbcfm.com/api/v1/image/107191457-1675888570906-gettyimages-1246870629-AFP_338Q79Q.jpeg?v=1693242001&w=1920&h=1080", "sourceWidth": 1920, "sourceHeight": 1080, "credit": "CNBC", "thumbnail": { "path": "/img/library/ref-e2b21038014e-small.webp", "width": 480, "height": 270 } } }, "summary": "Reporting on the infrastructure costs behind generative AI.", "curation": { "note": null, "listingNote": "Reporting on the infrastructure costs behind generative AI." }, "topics": [ "Company building" ], "tags": [ "Economics" ], "related": [ { "id": "counting-the-cost-of-training-large-language-models", "reason": "Training-cost analysis examines one component of the infrastructure economics." }, { "id": "the-inference-cost-of-search-disruption-large-language-model-cost-analysis", "reason": "Inference economics explains the recurring cost of serving language-model requests." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/chatgpt-and-generative-ai-are-booming-but-the-costs-can-be-extraordinary", "status": "curated" }, { "id": "lespace-indopacifique-une-priorite-pour-la-france-france-diplomatie", "slug": "lespace-indopacifique-une-priorite-pour-la-france-france-diplomatie", "source": { "title": "L’espace indopacifique : une priorité pour la France | France Diplomatie", "type": "article", "canonicalUrl": "https://www.diplomatie.gouv.fr/fr/priorites-et-actions/grands-dossiers/la-strategie-de-la-france-dans-l-indopacifique/l-espace-indopacifique-une-priorite-pour-la-france", "authors": [], "publisher": { "name": "diplomatie.gouv.fr", "url": "https://www.diplomatie.gouv.fr" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://www.diplomatie.gouv.fr/fr/priorites-et-actions/grands-dossiers/la-strategie-de-la-france-dans-l-indopacifique/l-espace-indopacifique-une-priorite-pour-la-france", "credit": "diplomatie.gouv.fr", "kind": "source-image", "alt": "Map highlighting the Indo-Pacific region", "sourceWidth": 1200, "sourceHeight": 675, "path": "/img/library/lespace-indopacifique-une-priorite-pour-la-france-france-diplomatie.webp", "width": 1200, "height": 675, "thumbnail": { "path": "/img/library/lespace-indopacifique-une-priorite-pour-la-france-france-diplomatie-small.webp", "width": 480, "height": 270 }, "sourceImage": "https://www.diplomatie.gouv.fr/files/styles/ds_image_paragraphe/public/files/priorites-et-actions/grands-dossiers/indopacifique.jpg?itok=HVByfhew" } }, "summary": "France’s diplomatic priorities and regional partnerships in the Indo-Pacific.", "curation": { "note": null, "listingNote": "France’s diplomatic priorities and regional partnerships in the Indo-Pacific." }, "topics": [ "Engineering" ], "tags": [ "Semiconductors" ], "related": [ { "id": "la-strategie-indopacifique-de-la-france", "reason": "The full national strategy develops the priorities introduced by this diplomatic overview." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/lespace-indopacifique-une-priorite-pour-la-france-france-diplomatie", "status": "curated", "citedIn": [ { "title": "LLM : souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "improved-text-scoring-with-bm25", "slug": "improved-text-scoring-with-bm25", "source": { "title": "Improved Text Scoring with BM25", "type": "article", "canonicalUrl": "https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25", "authors": [], "publisher": { "name": "Elastic", "url": "https://www.elastic.co" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-bdd6300f4edf.webp", "width": 1200, "height": 675, "sourcePage": "https://www.elastic.co/elasticon/conf/2016/sf/improved-text-scoring-with-bm25", "sourceImage": "https://play.vidyard.com/Dp2BwbFrNTL1HXYPTGBpjP.jpg", "sourceWidth": 1280, "sourceHeight": 720, "credit": "Elastic", "thumbnail": { "path": "/img/library/ref-bdd6300f4edf-small.webp", "width": 480, "height": 270 } } }, "summary": "A technical presentation on BM25 relevance scoring in Elasticsearch.", "curation": { "note": null, "listingNote": "A technical presentation on BM25 relevance scoring in Elasticsearch." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval" ], "related": [ { "id": "introduction-information-retrieval", "reason": "The retrieval textbook supplies the probabilistic ranking foundations behind BM25." }, { "id": "welcome-to-apache-lucene", "reason": "Lucene is the search library underlying the Elasticsearch ecosystem discussed here." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/improved-text-scoring-with-bm25", "status": "curated" }, { "id": "beyond-benchmarks-2024", "slug": "beyond-benchmarks-2024", "source": { "title": "Beyond Benchmarks 2024", "type": "article", "canonicalUrl": "https://www.emcap.com/thoughts/beyond-benchmarks", "authors": [], "publisher": { "name": "emcap.com", "url": "https://www.emcap.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://www.emcap.com/thoughts/beyond-benchmarks", "credit": "emcap.com", "kind": "source-image", "alt": "Beyond Benchmarks report cover, May 2024, Emergence Capital", "sourceWidth": 1910, "sourceHeight": 1052, "path": "/img/library/beyond-benchmarks-2024.webp", "width": 1200, "height": 661, "thumbnail": { "path": "/img/library/beyond-benchmarks-2024-small.webp", "width": 480, "height": 264 }, "sourceImage": "https://cdn.prod.website-files.com/67d0b820a9e389b3a4bb52a1/682e33327edea04d417d8e68_67d956ace533b18ad25c68fc_Beyond_Benchmarks_2024_Cover_Page.png" } }, "summary": "A study of AI-model performance beyond conventional benchmark scores.", "curation": { "note": null, "listingNote": "A study of AI-model performance beyond conventional benchmark scores." }, "topics": [ "AI & machine learning" ], "tags": [ "Evaluation" ], "related": [ { "id": "llm-evals-and-benchmarking-hackerllama", "reason": "An evaluation overview provides context for comparing benchmark methods." }, { "id": "style-outweighs-substance-failure-modes-of-llm-judges-in-alignment-benchmarking", "reason": "Judge bias illustrates why a strong benchmark score can be misleading." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/beyond-benchmarks-2024", "status": "curated" }, { "id": "your-success-with-generative-ai-may-come-down-to-these-ux-decisions", "slug": "your-success-with-generative-ai-may-come-down-to-these-ux-decisions", "source": { "title": "Your Success with Generative AI May Come Down to These UX Decisions", "type": "article", "canonicalUrl": "https://www.emcap.com/thoughts/your-success-with-generative-ai-may-come-down-to-these-ux-decisions", "authors": [], "publisher": { "name": "emcap.com", "url": "https://www.emcap.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-ec2f9d382d3d.webp", "width": 1200, "height": 630, "sourcePage": "https://www.emcap.com/thoughts/your-success-with-generative-ai-may-come-down-to-these-ux-decisions", "sourceImage": "https://cdn.prod.website-files.com/67d0b820a9e389b3a4bb52a1/682e3350af04888572bee45d_68095a8507b5fc3ddd6c63e3_AI%2520UX%2520Decisions%2520(1).png", "sourceWidth": 1200, "sourceHeight": 630, "credit": "emcap.com", "thumbnail": { "path": "/img/library/ref-ec2f9d382d3d-small.webp", "width": 480, "height": 252 } } }, "summary": "Product-interface decisions that affect the usefulness and adoption of generative AI.", "curation": { "note": null, "listingNote": "Product-interface decisions that affect the usefulness and adoption of generative AI." }, "topics": [ "Product & design" ], "tags": [ "Product engineering" ], "related": [ { "id": "checklist-design", "reason": "Product checklists help translate interface principles into concrete design decisions." }, { "id": "switching-perspectives-on-generative-artificial-intelligence-a-design-view-for-humans", "reason": "A design view of human-AI co-creation complements the interface-level advice." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/your-success-with-generative-ai-may-come-down-to-these-ux-decisions", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "the-economics-of-llms", "slug": "the-economics-of-llms", "source": { "title": "The Economics of LLMs", "type": "article", "canonicalUrl": "https://www.encora.com/insights/the-economics-of-llms", "authors": [], "publisher": { "name": "Encora", "url": "https://www.encora.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "availability": "unavailable", "preview": { "sourcePage": "https://web.archive.org/web/20240815182730/https://www.encora.com/insights/the-economics-of-llms", "credit": "Encora / Rodrigo Vargas, via the Internet Archive", "kind": "screenshot", "alt": "Archived article The Economics of LLMs, showing the introduction and the cost of inference section", "caption": "Archived article screenshot, Internet Archive snapshot from 15 August 2024.", "capturedAt": "2026-10-13", "sourceWidth": 1200, "sourceHeight": 1000, "path": "/img/library/the-economics-of-llms.webp", "width": 1200, "height": 1000, "thumbnail": { "path": "/img/library/the-economics-of-llms-small.webp", "width": 480, "height": 400 } } }, "summary": "A comparison of cost factors in selecting and deploying language models.", "curation": { "note": null, "listingNote": "A comparison of cost factors in selecting and deploying language models." }, "topics": [ "Company building" ], "tags": [ "Economics" ], "related": [ { "id": "llm-economics-which-is-cheaper-to-deploy-chatgpt-vs-open-source-llms", "reason": "Compare hosted-model and self-hosted-model costs in a focused economic analysis." }, { "id": "finding-the-right-llm-for-your-business-a-practical-cost-benefit-analysis", "reason": "A cost-benefit framework connects those costs to business model selection." } ], "archive": { "snapshotUrl": "https://web.archive.org/web/20240815182730/https://www.encora.com/insights/the-economics-of-llms" }, "detailUrl": "https://fpaupier.fr/library/the-economics-of-llms", "status": "curated" }, { "id": "what-large-models-cost-you-there-is-no-free-ai-lunch", "slug": "what-large-models-cost-you-there-is-no-free-ai-lunch", "source": { "title": "What Large Models Cost You: There Is No Free AI Lunch", "type": "article", "canonicalUrl": "https://www.forbes.com/sites/craigsmith/2023/09/08/what-large-models-cost-you--there-is-no-free-ai-lunch/?sh=58d0c4794af7", "authors": [ { "name": "Craig S. Smith", "type": "Person" } ], "publisher": { "name": "Forbes", "url": "https://www.forbes.com" }, "datePublished": "2023-09-08", "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://web.archive.org/web/20231204053137/https://www.forbes.com/sites/craigsmith/2023/09/08/what-large-models-cost-you--there-is-no-free-ai-lunch/", "sourceImage": "https://imageio.forbes.com/specials-images/imageserve/64fb30f65900cea4b773b640/0x0.jpg?format=jpg&height=900&width=1600&fit=bounds", "credit": "Tom Stoddart / Getty Images, via Forbes", "kind": "source-image", "alt": "Burning dollar banknotes in the photograph accompanying Forbes’s article on model costs.", "sourceWidth": 1350, "sourceHeight": 900, "path": "/img/library/what-large-models-cost-you-there-is-no-free-ai-lunch.webp", "width": 1200, "height": 800, "thumbnail": { "path": "/img/library/what-large-models-cost-you-there-is-no-free-ai-lunch-small.webp", "width": 480, "height": 320 } } }, "summary": "An account of the financial and operational costs of building and running language models.", "curation": { "note": null, "listingNote": "An account of the financial and operational costs of building and running language models." }, "topics": [ "Company building" ], "tags": [ "Economics" ], "related": [ { "id": "counting-the-cost-of-training-large-language-models", "reason": "Training-cost analysis examines one part of the expenditure discussed here." }, { "id": "the-inference-cost-of-search-disruption-large-language-model-cost-analysis", "reason": "Inference-cost analysis addresses the continuing cost of using a model." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/what-large-models-cost-you-there-is-no-free-ai-lunch", "status": "curated" }, { "id": "what-is-a-context-window", "slug": "what-is-a-context-window", "source": { "title": "What is a context window?", "type": "article", "canonicalUrl": "https://www.ibm.com/think/topics/context-window", "authors": [ { "name": "Dave Bergmann", "type": "Person" } ], "publisher": { "name": "ibm.com", "url": "https://www.ibm.com" }, "datePublished": "2024-11-26", "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-f1bcd9a98170.webp", "width": 1200, "height": 800, "sourcePage": "https://www.ibm.com/think/topics/context-window", "sourceImage": "https://www.ibm.com/content/dam/worldwide-content/stock-assets/adb-stk/ul/g/c6/c0/adobestock_791411314.jpeg/_jcr_content/renditions/cq5dam.web.1280.1280.jpeg", "sourceWidth": 1280, "sourceHeight": 853, "credit": "ibm.com", "thumbnail": { "path": "/img/library/ref-f1bcd9a98170-small.webp", "width": 480, "height": 320 } } }, "summary": "An explanation of context windows and their constraints in language models.", "curation": { "note": null, "listingNote": "An explanation of context windows and their constraints in language models." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "lost-in-the-middle-how-language-models-use-long-contexts", "reason": "Long-context research shows why capacity alone does not guarantee reliable use of information." }, { "id": "qwen-qwen2-5-7b-instruct-1m", "reason": "A million-token model card provides a concrete example of an extended context window." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/what-is-a-context-window", "status": "curated" }, { "id": "what-is-a-llm-pricing-strategy", "slug": "what-is-a-llm-pricing-strategy", "source": { "title": "What is a LLM pricing strategy?", "type": "article", "canonicalUrl": "https://www.iguazio.com/questions/what-is-a-llm-pricing-strategy/", "authors": [], "publisher": { "name": "Iguazio", "url": "https://www.iguazio.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-e0f5e1e8fb18.webp", "width": 1200, "height": 675, "sourcePage": "https://www.iguazio.com/questions/what-is-a-llm-pricing-strategy/", "sourceImage": "https://www.iguazio.com/wp-content/uploads/2024/06/GEN-AI-Factory-B_Dark-BG.jpg", "sourceWidth": 1920, "sourceHeight": 1080, "credit": "Iguazio", "thumbnail": { "path": "/img/library/ref-e0f5e1e8fb18-small.webp", "width": 480, "height": 270 } } }, "summary": "An overview of pricing approaches for language-model services.", "curation": { "note": null, "listingNote": "An overview of pricing approaches for language-model services." }, "topics": [ "Company building" ], "tags": [ "Economics" ], "related": [ { "id": "genai-needs-pricing-strategies-to-match-its-potential", "reason": "The BCG framework connects pricing with customer value and differentiation." }, { "id": "llm-api-pricing-calculator", "reason": "An API calculator helps estimate the usage costs underlying a pricing model." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/what-is-a-llm-pricing-strategy", "status": "curated" }, { "id": "what-is-a-corporate-spin-off", "slug": "what-is-a-corporate-spin-off", "source": { "title": "What Is a Corporate Spin-Off?", "type": "article", "canonicalUrl": "https://www.investopedia.com/terms/s/spinoff.asp", "authors": [], "publisher": { "name": "Investopedia", "url": "https://www.investopedia.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://www.investopedia.com/terms/s/spinoff.asp", "credit": "Investopedia", "kind": "source-image", "alt": "Investopedia illustration defining a corporate spinoff", "sourceWidth": 1500, "sourceHeight": 1000, "path": "/img/library/what-is-a-corporate-spin-off.webp", "width": 1200, "height": 800, "thumbnail": { "path": "/img/library/what-is-a-corporate-spin-off-small.webp", "width": 480, "height": 320 }, "sourceImage": "https://www.investopedia.com/thmb/jpdEREz4cx2Zd1mY-HqdUc3QEeM=/1500x0/filters:no_upscale():max_bytes(150000):strip_icc()/TermDefinitions_Spinoff-25b358d4a4704fccad845bbee039cf9c.jpg" } }, "summary": "An explanation of corporate spin-offs and their financial structure.", "curation": { "note": null, "listingNote": "An explanation of corporate spin-offs and their financial structure." }, "topics": [ "Company building" ], "tags": [ "Economics" ], "related": [ { "id": "intel-announces-launch-of-enterprise-gen-ai-spinoff-articul8", "reason": "Articul8 supplies a concrete example of an enterprise-AI spin-off." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/what-is-a-corporate-spin-off", "status": "curated", "citedIn": [ { "title": "GenAI in Production: Lessons from RAG", "url": "https://fpaupier.fr/202409_genai_in_prod" } ] }, { "id": "searching-in-one-billion-vectors-re-rank-with-source-coding", "slug": "searching-in-one-billion-vectors-re-rank-with-source-coding", "source": { "title": "Searching in One Billion Vectors: Re-rank with Source Coding", "type": "paper", "canonicalUrl": "https://www.irisa.fr/texmex/people/jegou/papers/jegou_icassp11.pdf", "authors": [ { "name": "Hervé Jégou", "type": "Person" }, { "name": "Romain Tavenard", "type": "Person" }, { "name": "Matthijs Douze", "type": "Person" }, { "name": "Laurent Amsaleg", "type": "Person" } ], "publisher": { "name": "IRISA", "url": "https://www.irisa.fr" }, "datePublished": null, "access": "public", "schemaType": "ScholarlyArticle", "preview": { "sourcePage": "https://www.irisa.fr/texmex/people/jegou/papers/jegou_icassp11.pdf", "sourceImage": "https://www.irisa.fr/texmex/people/jegou/papers/jegou_icassp11.pdf", "credit": "Hervé Jégou, Romain Tavenard, Matthijs Douze, Laurent Amsaleg", "kind": "paper-first-page", "alt": "Title page of Searching in One Billion Vectors: Re-rank with Source Coding.", "sourceWidth": 1391, "sourceHeight": 1800, "path": "/img/library/searching-in-one-billion-vectors-re-rank-with-source-coding.webp", "width": 1200, "height": 1553, "thumbnail": { "path": "/img/library/searching-in-one-billion-vectors-re-rank-with-source-coding-small.webp", "width": 480, "height": 621 } } }, "summary": "A vector-search method that refines nearest-neighbor results using compact quantization codes.", "curation": { "note": null, "listingNote": "A vector-search method that refines nearest-neighbor results using compact quantization codes." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Model adaptation" ], "related": [ { "id": "product-quantization-for-nearest-neighbor-search", "reason": "Product quantization explains the compact-code approach used in large-scale vector search." }, { "id": "diskann-fast-accurate-billion-point-nearest-neighbor-search-on-a-single-node", "reason": "Compare another system targeting billion-point nearest-neighbor retrieval." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/searching-in-one-billion-vectors-re-rank-with-source-coding", "status": "curated" }, { "id": "the-diminishing-returns-of-deploying-local-large-language-models", "slug": "the-diminishing-returns-of-deploying-local-large-language-models", "source": { "title": "The Diminishing Returns of Deploying Local Large Language Models", "type": "article", "canonicalUrl": "https://www.linkedin.com/pulse/diminishing-returns-deploying-local-large-language-models-barton", "authors": [], "publisher": { "name": "linkedin.com", "url": "https://www.linkedin.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-967496b1f4af.webp", "width": 1200, "height": 290, "sourcePage": "https://www.linkedin.com/pulse/diminishing-returns-deploying-local-large-language-models-barton", "sourceImage": "https://media.licdn.com/dms/image/v2/D4E12AQFfA4da4PhkNQ/article-cover_image-shrink_720_1280/article-cover_image-shrink_720_1280/0/1693585810535?e=2147483647&v=beta&t=7P3uK8xGWDMYN9WrAeyfFtT9kjI-9e5YBKr8ikUrlaE", "sourceWidth": 1280, "sourceHeight": 309, "credit": "linkedin.com", "thumbnail": { "path": "/img/library/ref-967496b1f4af-small.webp", "width": 480, "height": 116 } } }, "summary": "An analysis of diminishing returns in deploying language models locally.", "curation": { "note": null, "listingNote": "An analysis of diminishing returns in deploying language models locally." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "llm-economics-which-is-cheaper-to-deploy-chatgpt-vs-open-source-llms", "reason": "A hosted-versus-self-hosted comparison develops the cost trade-offs of local deployment." }, { "id": "ollama-run-ai-models-locally-and-in-the-cloud", "reason": "A local-model runtime is one concrete option whose trade-offs can be assessed." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/the-diminishing-returns-of-deploying-local-large-language-models", "status": "curated" }, { "id": "llm-economics-which-is-cheaper-to-deploy-chatgpt-vs-open-source-llms", "slug": "llm-economics-which-is-cheaper-to-deploy-chatgpt-vs-open-source-llms", "source": { "title": "LLM Economics: Which is Cheaper to deploy ChatGPT Vs Open Source LLMs?", "type": "article", "canonicalUrl": "https://www.linkedin.com/pulse/llm-economics-which-cheaper-deploy-open-source-llms-openai-nawaz", "authors": [], "publisher": { "name": "linkedin.com", "url": "https://www.linkedin.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-610866d5aa2d.webp", "width": 900, "height": 556, "sourcePage": "https://www.linkedin.com/pulse/llm-economics-which-cheaper-deploy-open-source-llms-openai-nawaz", "sourceImage": "https://media.licdn.com/dms/image/v2/D5612AQEtqLdi2cKFTw/article-cover_image-shrink_600_2000/article-cover_image-shrink_600_2000/0/1691556594172?e=2147483647&v=beta&t=7mfLpEhvigAikS-Gs8imLDyr5GlcHiBY4tFjrgC4r_Q", "sourceWidth": 900, "sourceHeight": 556, "credit": "linkedin.com", "thumbnail": { "path": "/img/library/ref-610866d5aa2d-small.webp", "width": 480, "height": 297 } } }, "summary": "A cost comparison between hosted commercial models and self-hosted open models.", "curation": { "note": null, "listingNote": "A cost comparison between hosted commercial models and self-hosted open models." }, "topics": [ "Company building", "Engineering" ], "tags": [ "Economics", "Open source" ], "related": [ { "id": "the-economics-of-llms", "reason": "A broader comparison develops the cost factors behind language-model deployment." }, { "id": "cost-optimized-hosting-of-fine-tuned-llms-in-production-microsoft-community-hub", "reason": "Serving optimizations can change the economics of self-hosted models." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/llm-economics-which-is-cheaper-to-deploy-chatgpt-vs-open-source-llms", "status": "curated" }, { "id": "how-to-choose-the-best-embedding-model-for-your-llm-application", "slug": "how-to-choose-the-best-embedding-model-for-your-llm-application", "source": { "title": "How to Choose the Best Embedding Model for Your LLM Application", "type": "article", "canonicalUrl": "https://www.mongodb.com/developer/products/atlas/choose-embedding-model-rag/", "authors": [ { "name": "Apoorva Joshi", "type": "Person" } ], "publisher": { "name": "MongoDB", "url": "https://www.mongodb.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "availability": "unavailable", "preview": { "sourcePage": "https://www.mongodb.com/company/blog/technical/how-choose-best-embedding-model-for-your-llm-application", "credit": "MongoDB", "kind": "source-image", "alt": "Diagram placing related words and objects close together in a two-dimensional embedding space", "sourceWidth": 1324, "sourceHeight": 778, "path": "/img/library/how-to-choose-the-best-embedding-model-for-your-llm-application.webp", "width": 1200, "height": 705, "thumbnail": { "path": "/img/library/how-to-choose-the-best-embedding-model-for-your-llm-application-small.webp", "width": 480, "height": 282 }, "sourceImage": "https://images.contentstack.io/v3/assets/blt7151619cb9560896/blt7b858c89ca8a7fe4/690546779e47f738fcc259ef/Screenshot_2025-10-10_at_9.00.30_AM-9o6ijfclpm.png" } }, "summary": "Guidance on choosing an embedding model for an application that uses language models.", "curation": { "note": null, "listingNote": "Guidance on choosing an embedding model for an application that uses language models." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval" ], "related": [ { "id": "mteb-leaderboard-a-hugging-face-space-by-mteb", "reason": "Benchmark results provide one source of evidence for comparing candidate embedding models." }, { "id": "recent-advances-in-text-embedding-a-comprehensive-review-of-top-performing-methods-on", "reason": "The review explains methods behind strong embedding-benchmark results." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/how-to-choose-the-best-embedding-model-for-your-llm-application", "status": "curated" }, { "id": "user-intent-and-implicit-feedback-in-ai-agents-what-every-enterprise-leader-needs-to", "slug": "user-intent-and-implicit-feedback-in-ai-agents-what-every-enterprise-leader-needs-to", "source": { "title": "User intent and implicit feedback in AI agents: what every enterprise leader needs to know - Nebuly - The ROI platform for Enterprise AI", "type": "article", "canonicalUrl": "https://www.nebuly.com/blog/user-intent-and-implicit-feedback-in-conversational-ai-a-complete-guide", "authors": [], "publisher": { "name": "nebuly.com", "url": "https://www.nebuly.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-eeea95ff1d73.webp", "width": 1200, "height": 675, "sourcePage": "https://www.nebuly.com/blog/user-intent-and-implicit-feedback-in-conversational-ai-a-complete-guide", "sourceImage": "https://framerusercontent.com/images/XtQbgS6PVFMgDxdqoTNBzZL8py4.png?width=3840&height=2160", "sourceWidth": 3840, "sourceHeight": 2160, "credit": "nebuly.com", "thumbnail": { "path": "/img/library/ref-eeea95ff1d73-small.webp", "width": 480, "height": 270 } } }, "summary": "An explanation of explicit and implicit user feedback for AI products.", "curation": { "note": null, "listingNote": "An explanation of explicit and implicit user feedback for AI products." }, "topics": [ "AI & machine learning", "Product & design" ], "tags": [ "Agents", "Product engineering" ], "related": [ { "id": "ai-engineering-flywheel", "reason": "User signals can feed the monitoring and evaluation cycle of an AI application." }, { "id": "feedback-functions", "reason": "Feedback functions offer another approach to measuring application behavior." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/user-intent-and-implicit-feedback-in-ai-agents-what-every-enterprise-leader-needs-to", "status": "curated" }, { "id": "counting-the-cost-of-training-large-language-models", "slug": "counting-the-cost-of-training-large-language-models", "source": { "title": "Counting The Cost Of Training Large Language Models", "type": "article", "canonicalUrl": "https://www.nextplatform.com/ai/2022/12/01/counting-the-cost-of-training-large-language-models/1633617", "authors": [], "publisher": { "name": "nextplatform", "url": "https://www.nextplatform.com" }, "datePublished": "2022-12-01", "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://www.nextplatform.com/ai/2022/12/01/counting-the-cost-of-training-large-language-models/1633617", "credit": "nextplatform", "kind": "source-image", "alt": "Table comparing traditional cloud and custom AI model builder infrastructure", "sourceWidth": 960, "sourceHeight": 310, "path": "/img/library/counting-the-cost-of-training-large-language-models.webp", "width": 960, "height": 310, "thumbnail": { "path": "/img/library/counting-the-cost-of-training-large-language-models-small.webp", "width": 480, "height": 155 }, "sourceImage": "https://image.nextplatform.com/216091.webp?imageId=216091&width=960&height=310&format=jpg" } }, "summary": "An analysis of the cost of training large language models.", "curation": { "note": null, "listingNote": "An analysis of the cost of training large language models." }, "topics": [ "Company building" ], "tags": [ "Economics" ], "related": [ { "id": "what-large-models-cost-you-there-is-no-free-ai-lunch", "reason": "A broader account places training expenditure alongside other model costs." }, { "id": "a-survey-of-sustainability-in-large-language-models-applications-economics-and-challe", "reason": "Sustainability research connects computation and resource requirements with economic concerns." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/counting-the-cost-of-training-large-language-models", "status": "curated" }, { "id": "nvidia-a100-tensor-core-gpu-datasheet", "slug": "nvidia-a100-tensor-core-gpu-datasheet", "source": { "title": "NVIDIA A100 Tensor Core GPU datasheet", "type": "documentation", "canonicalUrl": "https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/a100/pdf/nvidia-a100-datasheet-us-nvidia-1758950-r4-web.pdf", "authors": [], "publisher": { "name": "NVIDIA", "url": "https://www.nvidia.com" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/a100/pdf/nvidia-a100-datasheet-us-nvidia-1758950-r4-web.pdf", "sourceImage": "https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/a100/pdf/nvidia-a100-datasheet-us-nvidia-1758950-r4-web.pdf", "credit": "NVIDIA", "kind": "document-page", "alt": "First page of NVIDIA A100 Tensor Core GPU datasheet.", "sourceWidth": 1391, "sourceHeight": 1800, "path": "/img/library/nvidia-a100-tensor-core-gpu-datasheet.webp", "width": 1200, "height": 1553, "thumbnail": { "path": "/img/library/nvidia-a100-tensor-core-gpu-datasheet-small.webp", "width": 480, "height": 621 }, "pageNumber": 1 } }, "summary": "NVIDIA’s technical datasheet for the A100 Tensor Core GPU.", "curation": { "note": null, "listingNote": "NVIDIA’s technical datasheet for the A100 Tensor Core GPU." }, "topics": [ "Engineering" ], "tags": [ "Inference" ], "related": [ { "id": "nvidia-h100-gpu", "reason": "Compare the A100 specifications with a later H100 accelerator." }, { "id": "nvidia-system-management-interface-nvidia-smi", "reason": "NVIDIA’s management interface helps inspect the deployed hardware." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/nvidia-a100-tensor-core-gpu-datasheet", "status": "curated" }, { "id": "nvidia-l40-datasheet", "slug": "nvidia-l40-datasheet", "source": { "title": "NVIDIA L40 datasheet", "type": "documentation", "canonicalUrl": "https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/support-guide/NVIDIA-L40-Datasheet-January-2023.pdf", "authors": [ { "name": "NVIDIA Corporation", "type": "Organization" } ], "publisher": { "name": "NVIDIA", "url": "https://www.nvidia.com" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/support-guide/NVIDIA-L40-Datasheet-January-2023.pdf", "sourceImage": "https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/support-guide/NVIDIA-L40-Datasheet-January-2023.pdf", "credit": "NVIDIA Corporation", "kind": "document-page", "alt": "First page of NVIDIA L40 datasheet.", "sourceWidth": 1391, "sourceHeight": 1800, "path": "/img/library/nvidia-l40-datasheet.webp", "width": 1200, "height": 1553, "thumbnail": { "path": "/img/library/nvidia-l40-datasheet-small.webp", "width": 480, "height": 621 }, "pageNumber": 1 } }, "summary": "NVIDIA’s technical datasheet for the L40 GPU.", "curation": { "note": null, "listingNote": "NVIDIA’s technical datasheet for the L40 GPU." }, "topics": [ "Engineering" ], "tags": [ "Inference" ], "related": [ { "id": "nvidia-h100-gpu", "reason": "Compare the L40 datasheet with specifications for another NVIDIA accelerator." }, { "id": "nvidia-system-management-interface-nvidia-smi", "reason": "The management-interface reference explains how GPU state can be inspected." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/nvidia-l40-datasheet", "status": "curated" }, { "id": "hierarchical-navigable-small-worlds-hnsw-pinecone", "slug": "hierarchical-navigable-small-worlds-hnsw-pinecone", "source": { "title": "Hierarchical Navigable Small Worlds (HNSW) | Pinecone", "type": "article", "canonicalUrl": "https://www.pinecone.io/learn/series/faiss/hnsw/", "authors": [], "publisher": { "name": "pinecone.io", "url": "https://www.pinecone.io" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-249863a5b898.webp", "width": 1200, "height": 630, "sourcePage": "https://www.pinecone.io/learn/series/faiss/hnsw/", "sourceImage": "https://www.pinecone.io/api/og/?title=Hierarchical%20Navigable%20Small%20Worlds%20(HNSW)&category=Learn", "sourceWidth": 1200, "sourceHeight": 630, "credit": "pinecone.io", "thumbnail": { "path": "/img/library/ref-249863a5b898-small.webp", "width": 480, "height": 252 } } }, "summary": "An illustrated explanation of HNSW vector-search indexes.", "curation": { "note": null, "listingNote": "An illustrated explanation of HNSW vector-search indexes." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval" ], "related": [ { "id": "efficient-and-robust-approximate-nearest-neighbor-search-using-hierarchical-navigable", "reason": "The original HNSW paper provides the formal algorithmic account." }, { "id": "filterable-hnsw-without-recall-loss", "reason": "Filtered HNSW extends the discussion to constrained retrieval." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/hierarchical-navigable-small-worlds-hnsw-pinecone", "status": "curated" }, { "id": "7-top-large-language-model-use-cases-and-applications", "slug": "7-top-large-language-model-use-cases-and-applications", "source": { "title": "7 Top Large Language Model Use Cases And Applications", "type": "article", "canonicalUrl": "https://www.projectpro.io/article/large-language-model-use-cases-and-applications/887", "authors": [], "publisher": { "name": "ProjectPro", "url": "https://www.projectpro.io" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-ee75b7347a22.webp", "width": 1200, "height": 720, "sourcePage": "https://www.projectpro.io/article/large-language-model-use-cases-and-applications/887", "sourceImage": "https://daxg39y63pxwu.cloudfront.net/images/blog/large-language-model-use-cases-and-applications/Large_Language_Model_Use_Cases.webp", "sourceWidth": 2084, "sourceHeight": 1250, "credit": "ProjectPro", "thumbnail": { "path": "/img/library/ref-ee75b7347a22-small.webp", "width": 480, "height": 288 } } }, "summary": "Examples of business applications for large language models.", "curation": { "note": null, "listingNote": "Examples of business applications for large language models." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "qonto-how-qonto-achieved-70-faster-localization-and-streamlined-operations-with-ai-ag", "reason": "Qonto’s deployment offers a concrete operational AI-agent example." }, { "id": "gen-ai-at-work-the-story-of-carrefour-marketing-studio-horizons-by-carrefour", "reason": "Carrefour’s marketing studio provides another specific business application." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/7-top-large-language-model-use-cases-and-applications", "status": "curated" }, { "id": "using-object-storage-with-s3cmd", "slug": "using-object-storage-with-s3cmd", "source": { "title": "Using Object Storage with s3cmd", "type": "documentation", "canonicalUrl": "https://www.scaleway.com/en/docs/tutorials/s3cmd/", "authors": [], "publisher": { "name": "Scaleway", "url": "https://www.scaleway.com" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://www.scaleway.com/en/docs/tutorials/s3cmd/", "credit": "Scaleway", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "Scaleway guide showing prerequisites and installation commands for S3cmd object storage.", "sourceWidth": 1000, "sourceHeight": 900, "path": "/img/library/using-object-storage-with-s3cmd.webp", "width": 1000, "height": 900, "thumbnail": { "path": "/img/library/using-object-storage-with-s3cmd-small.webp", "width": 480, "height": 432 } } }, "summary": "A guide to configuring s3cmd for Scaleway Object Storage. Covers regional credentials and configuration files, bucket and object operations, and CORS configuration.", "curation": { "note": null, "listingNote": "A guide to configuring s3cmd for Scaleway Object Storage. Covers regional credentials and configuration files, bucket and object operations, and CORS configuration." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "object-storage-utiliser-object-storage-avec-s3cmd-ovhcloud-documentation", "reason": "Compare the same S3cmd workflow configured for OVHcloud object storage." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/using-object-storage-with-s3cmd", "status": "curated" }, { "id": "google-we-have-no-moat-and-neither-does-openai", "slug": "google-we-have-no-moat-and-neither-does-openai", "source": { "title": "Google \"We Have No Moat, And Neither Does OpenAI\"", "type": "article", "canonicalUrl": "https://newsletter.semianalysis.com/p/google-we-have-no-moat-and-neither", "authors": [ { "name": "Dylan Patel", "type": "Person" } ], "publisher": { "name": "newsletter.semianalysis.com", "url": "https://newsletter.semianalysis.com" }, "datePublished": null, "access": "restricted", "schemaType": "Article", "preview": { "path": "/img/library/ref-26a3853e3714.webp", "width": 1200, "height": 588, "sourcePage": "https://newsletter.semianalysis.com/p/google-we-have-no-moat-and-neither", "sourceImage": "https://substackcdn.com/image/fetch/$s_!_tGS!,w_1200,h_675,c_fill,f_jpg,q_auto:good,fl_progressive:steep,g_auto/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F241fe3ef-3919-4a63-9c68-9e2e77cc2fc0_1366x588.png", "sourceWidth": 1200, "sourceHeight": 588, "credit": "semianalysis.com", "thumbnail": { "path": "/img/library/ref-26a3853e3714-small.webp", "width": 480, "height": 235 } } }, "summary": "A discussion of open models and competitive defensibility in AI.", "curation": { "note": null, "listingNote": "A discussion of open models and competitive defensibility in AI." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "what-to-consider-with-an-open-source-business-model", "reason": "Open-source business models provide context for the debate about competitive defensibility." }, { "id": "opening-up-chatgpt", "reason": "Openness criteria clarify what an open-model competitive landscape actually includes." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/google-we-have-no-moat-and-neither-does-openai", "status": "curated" }, { "id": "the-inference-cost-of-search-disruption-large-language-model-cost-analysis", "slug": "the-inference-cost-of-search-disruption-large-language-model-cost-analysis", "source": { "title": "The Inference Cost Of Search Disruption – Large Language Model Cost Analysis", "type": "article", "canonicalUrl": "https://newsletter.semianalysis.com/p/the-inference-cost-of-search-disruption", "authors": [ { "name": "Dylan Patel", "type": "Person" } ], "publisher": { "name": "newsletter.semianalysis.com", "url": "https://newsletter.semianalysis.com" }, "datePublished": null, "access": "restricted", "schemaType": "Article", "preview": { "path": "/img/library/ref-77ccb2e65114.webp", "width": 1200, "height": 675, "sourcePage": "https://newsletter.semianalysis.com/p/the-inference-cost-of-search-disruption", "sourceImage": "https://substackcdn.com/image/fetch/$s_!8IRx!,w_1200,h_675,c_fill,f_jpg,q_auto:good,fl_progressive:steep,g_auto/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F281a8cd2-7a1a-4cf2-aba2-fffdf9d46c54_1886x954.jpeg", "sourceWidth": 1200, "sourceHeight": 675, "credit": "semianalysis.com", "thumbnail": { "path": "/img/library/ref-77ccb2e65114-small.webp", "width": 480, "height": 270 } } }, "summary": "An analysis of the inference economics of language-model-based search.", "curation": { "note": null, "listingNote": "An analysis of the inference economics of language-model-based search." }, "topics": [ "Engineering", "Company building" ], "tags": [ "Inference", "Economics" ], "related": [ { "id": "llm-inference-performance", "reason": "Inference engineering explains the performance levers behind serving economics." }, { "id": "the-economics-of-llms", "reason": "A broader cost comparison places search inference within model-deployment choices." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/the-inference-cost-of-search-disruption-large-language-model-cost-analysis", "status": "curated" }, { "id": "us-semiconductor-ecosystem-map", "slug": "us-semiconductor-ecosystem-map", "source": { "title": "US Semiconductor Ecosystem Map", "type": "website", "canonicalUrl": "https://www.semiconductors.org/ecosystem/", "authors": [], "publisher": { "name": "Semiconductor Industry Association", "url": "https://www.semiconductors.org" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-490e24f7a80d.webp", "width": 1200, "height": 300, "sourcePage": "https://www.semiconductors.org/ecosystem/", "sourceImage": "https://www.semiconductors.org/wp-content/uploads/2020/06/Strengthening_hero-image.png", "sourceWidth": 1400, "sourceHeight": 350, "credit": "Semiconductor Industry Association", "thumbnail": { "path": "/img/library/ref-490e24f7a80d-small.webp", "width": 480, "height": 120 } } }, "summary": "A map of the US semiconductor ecosystem.", "curation": { "note": null, "listingNote": "A map of the US semiconductor ecosystem." }, "topics": [ "Engineering" ], "tags": [ "Semiconductors" ], "related": [ { "id": "semiconductor-resilience", "reason": "The supply-chain report adds analysis to the map’s geographic view of the industry." }, { "id": "fact-sheet-chips-and-science-act-will-lower-costs-create-jobs-strengthen-supply-chain", "reason": "US semiconductor policy helps explain investment priorities within the ecosystem." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/us-semiconductor-ecosystem-map", "status": "curated", "citedIn": [ { "title": "LLM : souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "fine-tuning-large-language-models-llms-in-2026", "slug": "fine-tuning-large-language-models-llms-in-2026", "source": { "title": "Fine-tuning large language models (LLMs) in 2026", "type": "article", "canonicalUrl": "https://www.superannotate.com/blog/llm-fine-tuning", "authors": [], "publisher": { "name": "SuperAnnotate", "url": "https://www.superannotate.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-c6f8cf686b4a.webp", "width": 1200, "height": 750, "sourcePage": "https://www.superannotate.com/blog/llm-fine-tuning", "sourceImage": "https://cdn.sanity.io/images/f9xfqka1/production/342cc58c22f70997da34bc8b0d6202416303c138-2400x1500.webp?w=1200&auto=format&fit=max", "sourceWidth": 1200, "sourceHeight": 750, "credit": "SuperAnnotate", "thumbnail": { "path": "/img/library/ref-c6f8cf686b4a-small.webp", "width": 480, "height": 300 } } }, "summary": "A guide to adapting large language models through fine-tuning.", "curation": { "note": null, "listingNote": "A guide to adapting large language models through fine-tuning." }, "topics": [ "AI & machine learning" ], "tags": [ "Model adaptation" ], "related": [ { "id": "easily-train-a-specialized-llm-peft-lora-qlora-llama-adapter-and-more", "reason": "Compare the overview with a guide to specific parameter-efficient methods." }, { "id": "practical-tips-for-finetuning-llms-using-lora-low-rank-adaptation", "reason": "Practical LoRA experiments provide a focused adaptation case study." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/fine-tuning-large-language-models-llms-in-2026", "status": "curated" }, { "id": "the-360k-question-about-large-language-models-economics", "slug": "the-360k-question-about-large-language-models-economics", "source": { "title": "The $360K question about Large Language Models Economics", "type": "article", "canonicalUrl": "https://www.truefoundry.com/blog/economics-of-large-language-models", "authors": [], "publisher": { "name": "truefoundry.com", "url": "https://www.truefoundry.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "path": "/img/library/ref-86bce7d55d7c.webp", "width": 1200, "height": 675, "sourcePage": "https://www.truefoundry.com/blog/economics-of-large-language-models", "sourceImage": "https://cdn.prod.website-files.com/6295808d44499cde2ba36c71/65cb84ff2e7c90b26c8f2b16_The--360K-question-about-Large-L.webp", "sourceWidth": 1500, "sourceHeight": 844, "credit": "truefoundry.com", "thumbnail": { "path": "/img/library/ref-86bce7d55d7c-small.webp", "width": 480, "height": 270 } } }, "summary": "An analysis of costs and tradeoffs in running large language models.", "curation": { "note": null, "listingNote": "An analysis of costs and tradeoffs in running large language models." }, "topics": [ "Company building" ], "tags": [ "Economics" ], "related": [ { "id": "llm-economics-which-is-cheaper-to-deploy-chatgpt-vs-open-source-llms", "reason": "Compare another analysis of hosted and self-hosted language-model economics." }, { "id": "cost-optimized-hosting-of-fine-tuned-llms-in-production-microsoft-community-hub", "reason": "Cost-optimized serving shows how implementation choices affect the calculation." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/the-360k-question-about-large-language-models-economics", "status": "curated" }, { "id": "trulens-evals-and-tracing-for-ai-agents", "slug": "trulens-evals-and-tracing-for-ai-agents", "source": { "title": "TruLens: Evals and Tracing for AI Agents", "type": "website", "canonicalUrl": "https://www.trulens.org/", "authors": [ { "name": "Snowflake", "type": "Organization" } ], "publisher": { "name": "TruLens", "url": "https://www.trulens.org" }, "datePublished": null, "access": "public", "schemaType": "WebSite", "preview": { "path": "/img/library/ref-56ba1552985c.webp", "width": 1200, "height": 630, "sourcePage": "https://www.trulens.org/", "sourceImage": "https://www.trulens.org/assets/images/og-card.png", "sourceWidth": 1200, "sourceHeight": 630, "credit": "TruLens", "thumbnail": { "path": "/img/library/ref-56ba1552985c-small.webp", "width": 480, "height": 252 } } }, "summary": "Documentation and examples for tracing and evaluating AI applications with TruLens.", "curation": { "note": null, "listingNote": "Documentation and examples for tracing and evaluating AI applications with TruLens." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Evaluation", "Observability", "Agents" ], "related": [ { "id": "feedback-functions", "reason": "Feedback functions explain the evaluation concepts used by TruLens." }, { "id": "the-rag-triad", "reason": "The RAG triad applies those concepts to retrieval-augmented generation." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/trulens-evals-and-tracing-for-ai-agents", "status": "curated" }, { "id": "feedback-functions", "slug": "feedback-functions", "source": { "title": "Feedback Functions", "type": "documentation", "canonicalUrl": "https://www.trulens.org/getting_started/core_concepts/feedback_functions/", "authors": [], "publisher": { "name": "TruLens", "url": "https://www.trulens.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://www.trulens.org/getting_started/core_concepts/feedback_functions/", "sourceImage": "https://www.trulens.org/assets/images/Range_of_Feedback_Functions.png", "credit": "TruLens", "kind": "source-image", "alt": "Chart comparing evaluation feedback methods by scalability and meaning.", "sourceWidth": 2614, "sourceHeight": 1564, "path": "/img/library/feedback-functions.webp", "width": 1200, "height": 718, "thumbnail": { "path": "/img/library/feedback-functions-small.webp", "width": 480, "height": 287 } } }, "summary": "Introduces programmatic feedback functions for evaluating LLM application runs. Compares expert judgments, user feedback, traditional NLP metrics, and model-based evaluations by scalability and usefulness.", "curation": { "note": null, "listingNote": "Introduces programmatic feedback functions for evaluating LLM application runs. Compares expert judgments, user feedback, traditional NLP metrics, and model-based evaluations by scalability and usefulness." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Evaluation", "Observability", "Product engineering" ], "related": [ { "id": "trulens-evals-and-tracing-for-ai-agents", "reason": "The TruLens project provides tooling for the evaluation methods described here." }, { "id": "likert-scale", "reason": "Human rating scales supply one perspective on collecting evaluation judgments." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/feedback-functions", "status": "curated" }, { "id": "the-rag-triad", "slug": "the-rag-triad", "source": { "title": "The RAG Triad", "type": "documentation", "canonicalUrl": "https://www.trulens.org/getting_started/core_concepts/rag_triad/", "authors": [], "publisher": { "name": "TruLens", "url": "https://www.trulens.org" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://www.trulens.org/getting_started/core_concepts/rag_triad/", "sourceImage": "https://www.trulens.org/assets/images/RAG_Triad.png", "credit": "TruLens", "kind": "source-image", "alt": "The RAG triad connects query, context and response through relevance and groundedness evaluations.", "sourceWidth": 1921, "sourceHeight": 1099, "path": "/img/library/the-rag-triad.webp", "width": 1200, "height": 687, "thumbnail": { "path": "/img/library/the-rag-triad-small.webp", "width": 480, "height": 275 } } }, "summary": "An explanation of groundedness, relevance, and context quality in RAG evaluation.", "curation": { "note": null, "listingNote": "An explanation of groundedness, relevance, and context quality in RAG evaluation." }, "topics": [ "AI & machine learning" ], "tags": [ "Retrieval", "Evaluation" ], "related": [ { "id": "ragas-automated-evaluation-of-retrieval-augmented-generation", "reason": "Ragas offers a complementary framework for evaluating retrieval-augmented generation." }, { "id": "contextcite", "reason": "Context attribution provides a more targeted way to inspect support for generated statements." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/the-rag-triad", "status": "curated" }, { "id": "la-start-up-francaise-mistral-ai-leve-105-millions-d-euros-pour-developper-des-modele", "slug": "la-start-up-francaise-mistral-ai-leve-105-millions-d-euros-pour-developper-des-modele", "source": { "title": "La start-up française Mistral AI lève 105 millions d'euros pour développer des modèles d'IA générative", "type": "article", "canonicalUrl": "https://www.usine-digitale.fr/article/la-start-up-francaise-mistral-ai-leve-105-millions-d-euros-pour-developper-des-modeles-d-ia-generative.N2142627", "authors": [ { "name": "Julien Bergounhoux", "type": "Person" } ], "publisher": { "name": "L’Usine Digitale", "url": "https://www.usine-digitale.fr" }, "datePublished": "2023-06-14", "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://www.usine-digitale.fr/article/la-start-up-francaise-mistral-ai-leve-105-millions-d-euros-pour-developper-des-modeles-d-ia-generative.N2142627", "sourceImage": "https://www.usine-digitale.fr/resizer/v2/VTLRKPL4FZLURCK22IS3K7ILC4.jpg?auth=2ad0abb5c3f41b0f71c277a2715636bda157e89de4502acb12aa1265b0b11b49&height=488&smart=true&width=732", "credit": "David Atlan / L’Usine Digitale", "kind": "source-image", "alt": "Mistral AI cofounders Guillaume Lample, Arthur Mensch and Timothée Lacroix.", "sourceWidth": 732, "sourceHeight": 488, "path": "/img/library/la-start-up-francaise-mistral-ai-leve-105-millions-d-euros-pour-developper-des-modele.webp", "width": 732, "height": 488, "thumbnail": { "path": "/img/library/la-start-up-francaise-mistral-ai-leve-105-millions-d-euros-pour-developper-des-modele-small.webp", "width": 480, "height": 320 } } }, "summary": "Reporting on Mistral AI’s early fundraising and plans to develop generative-AI models.", "curation": { "note": null, "listingNote": "Reporting on Mistral AI’s early fundraising and plans to develop generative-AI models." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "europe-big-tech", "reason": "The financing story is a concrete case within Europe’s wider technology-industry trajectory." }, { "id": "mistral-small-3-1-mistral-ai", "reason": "A subsequent model announcement shows the company’s work after its early fundraising." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/la-start-up-francaise-mistral-ai-leve-105-millions-d-euros-pour-developper-des-modele", "status": "curated", "citedIn": [ { "title": "LLM : souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "lallemagne-espere-rebondir-apres-labandon-par-intel-de-son-projet-de-mega-usine-a-30", "slug": "lallemagne-espere-rebondir-apres-labandon-par-intel-de-son-projet-de-mega-usine-a-30", "source": { "title": "L’Allemagne espère rebondir après l’abandon par Intel de son projet de méga-usine à 30 milliards d'euros", "type": "article", "canonicalUrl": "https://www.usinenouvelle.com/article/l-allemagne-espere-rebondir-apres-l-abandon-par-intel-de-son-projet-de-mega-usine-a-30-milliards-d-euros.N2235791", "authors": [ { "name": "Gwenaëlle Deboutte", "type": "Person" } ], "publisher": { "name": "L’Usine Nouvelle", "url": "https://www.usinenouvelle.com" }, "datePublished": "2025-07-28", "access": "restricted", "schemaType": "Article", "preview": { "sourcePage": "https://www.usinenouvelle.com/article/l-allemagne-espere-rebondir-apres-l-abandon-par-intel-de-son-projet-de-mega-usine-a-30-milliards-d-euros.N2235791", "sourceImage": "https://cloudfront-eu-central-1.images.arcpublishing.com/infopro/BP3QCNF3FRIVDAARBZLNNVOLMM.jpg", "credit": "L'Usine Nouvelle / CC BY-SA 4.0", "kind": "source-image", "caption": "Intel headquarters in Santa Clara, the original article photograph. Resized and converted to WebP.", "alt": "Exterior of Intel headquarters in Santa Clara with the company sign in front of the building.", "sourceWidth": 2784, "sourceHeight": 1863, "path": "/img/library/lallemagne-espere-rebondir-apres-labandon-par-intel-de-son-projet-de-mega-usine-a-30.webp", "width": 1200, "height": 803, "thumbnail": { "path": "/img/library/lallemagne-espere-rebondir-apres-labandon-par-intel-de-son-projet-de-mega-usine-a-30-small.webp", "width": 480, "height": 321 } } }, "summary": "Reporting on Intel’s cancelled German fabrication plant and the implications for industrial policy.", "curation": { "note": null, "listingNote": "Reporting on Intel’s cancelled German fabrication plant and the implications for industrial policy." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "european-chips-act", "reason": "The European Chips Act provides policy context for the cancelled fabrication investment." }, { "id": "semiconductor-resilience", "reason": "The global supply-chain analysis examines the industrial dependencies at stake." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/lallemagne-espere-rebondir-apres-labandon-par-intel-de-son-projet-de-mega-usine-a-30", "status": "curated", "citedIn": [ { "title": "LLM : souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "normal-distribution-percentile-formula-graph-vaia", "slug": "normal-distribution-percentile-formula-graph-vaia", "source": { "title": "Normal Distribution Percentile: Formula & Graph | Vaia", "type": "article", "canonicalUrl": "https://www.vaia.com/en-us/explanations/math/statistics/normal-distribution-percentile/", "authors": [], "publisher": { "name": "Vaia", "url": "https://www.vaia.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://www.vaia.com/en-us/explanations/math/statistics/normal-distribution-percentile/", "credit": "Vaia", "kind": "screenshot", "alt": "Screenshot of Normal Distribution Percentile: Formula & Graph | Vaia", "sourceWidth": 1200, "sourceHeight": 780, "path": "/img/library/normal-distribution-percentile-formula-graph-vaia.webp", "width": 1200, "height": 780, "thumbnail": { "path": "/img/library/normal-distribution-percentile-formula-graph-vaia-small.webp", "width": 480, "height": 312 }, "capturedAt": "2026-10-13" } }, "summary": "A worked explanation of percentiles in a normal distribution.", "curation": { "note": null, "listingNote": "A worked explanation of percentiles in a normal distribution." }, "topics": [ "Engineering" ], "tags": [ "Knowledge graphs" ], "related": [ { "id": "quantile", "reason": "Quantiles provide the general statistical concept behind normal-distribution percentiles." } ], "archive": { "snapshotUrl": null }, "detailUrl": 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321 } } }, "summary": "The W3C primer introducing the RDF 1.1 data model.", "curation": { "note": null, "listingNote": "The W3C primer introducing the RDF 1.1 data model." }, "topics": [ "AI & machine learning" ], "tags": [ "Machine learning" ], "related": [ { "id": "injecting-knowledge-graphs-into-large-language-models", "reason": "Knowledge-graph integration provides a language-model application for structured graph data." }, { "id": "janusgraph-distributed-open-source-massively-scalable-graph-database", "reason": "A graph database offers a practical comparison of how graph information is stored." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/rdf-1-1-primer", "status": "curated" }, { "id": "nvidia-blackwell-the-journey-from-die-to-data-center", "slug": "nvidia-blackwell-the-journey-from-die-to-data-center", "source": { "title": "NVIDIA Blackwell: The Journey From Die to Data Center", "type": "video", "canonicalUrl": 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resources and emissions involved across the lifecycle of AI systems." }, "topics": [ "AI & machine learning" ], "tags": [ "Sustainability" ], "related": [ { "id": "ecologits-calculator", "reason": "The calculator turns environmental-impact considerations into usage estimates." }, { "id": "a-survey-of-sustainability-in-large-language-models-applications-economics-and-challe", "reason": "A research survey examines related environmental, economic, and computational challenges." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/the-environmental-impacts-of-ai-policy-primer", "status": "curated", "citedIn": [ { "title": "LLM : souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "open-container-initiative-charter", "slug": "open-container-initiative-charter", "source": { "title": "Open Container Initiative Charter", "type": "documentation", "canonicalUrl": "https://github.com/opencontainers/tob/blob/main/CHARTER.md", "authors": [], "publisher": { "name": "GitHub", "url": "https://github.com" }, "datePublished": null, "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://github.com/opencontainers/tob/blob/main/CHARTER.md", "credit": "Open Container Initiative / GitHub", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "Open Container Initiative charter on GitHub showing its mission and project governance.", "sourceWidth": 1200, "sourceHeight": 800, "path": "/img/library/open-container-initiative-charter.webp", "width": 1200, "height": 800, "thumbnail": { "path": "/img/library/open-container-initiative-charter-small.webp", "width": 480, "height": 320 } } }, "summary": "The governance charter defining the mission and operation of the Open Container Initiative.", "curation": { "note": null, "listingNote": "The governance charter defining the mission and operation of the Open Container Initiative." }, "topics": [ "Engineering" ], "tags": [ "Open source" ], "related": [ { "id": "bylaws-linux-foundation", "reason": "Compare two formal governance documents for open-source organizations." }, { "id": "opengovernance-opengovernance-dev", "reason": "The governance checklist provides questions to ask of a project charter." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/open-container-initiative-charter", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "bylaws-linux-foundation", "slug": "bylaws-linux-foundation", "source": { "title": "Bylaws | Linux Foundation", "type": "article", "canonicalUrl": "https://www.linuxfoundation.org/legal/bylaws", "authors": [], "publisher": { "name": "linuxfoundation.org", "url": "https://www.linuxfoundation.org" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://www.linuxfoundation.org/legal/bylaws", "credit": "linuxfoundation.org", "kind": "screenshot", "alt": "Screenshot of Bylaws | Linux Foundation", "sourceWidth": 1200, "sourceHeight": 500, "path": "/img/library/bylaws-linux-foundation.webp", "width": 1200, "height": 500, "thumbnail": { "path": "/img/library/bylaws-linux-foundation-small.webp", "width": 480, "height": 200 }, "capturedAt": "2026-10-13" } }, "summary": "The bylaws governing membership, leadership, and decision-making at the Linux Foundation.", "curation": { "note": null, "listingNote": "The bylaws governing membership, leadership, and decision-making at the Linux Foundation." }, "topics": [ "Engineering", "Product & design" ], "tags": [ "Open source", "Product engineering" ], "related": [ { "id": "a-primer-on-asf-governance-apache-software-foundation", "reason": "Compare the Linux Foundation’s bylaws with the Apache governance model." }, { "id": "open-container-initiative-charter", "reason": "The Open Container Initiative charter offers another formal governance example." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/bylaws-linux-foundation", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "apple-releases-swift-as-open-source", "slug": "apple-releases-swift-as-open-source", "source": { "title": "Apple Releases Swift as Open Source", "type": "article", "canonicalUrl": "https://www.apple.com/newsroom/2015/12/03Apple-Releases-Swift-as-Open-Source/", "authors": [], "publisher": { "name": "Apple Newsroom", "url": "https://www.apple.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://www.apple.com/newsroom/2015/12/03Apple-Releases-Swift-as-Open-Source/", "credit": "Apple Newsroom", "kind": "screenshot", "alt": "Screenshot of Apple Releases Swift as Open Source", "sourceWidth": 1200, "sourceHeight": 780, "path": "/img/library/apple-releases-swift-as-open-source.webp", "width": 1200, "height": 780, "thumbnail": { "path": "/img/library/apple-releases-swift-as-open-source-small.webp", "width": 480, "height": 312 }, "capturedAt": "2026-10-13" } }, "summary": "Apple’s announcement that Swift became an open-source programming language.", "curation": { "note": null, "listingNote": "Apple’s announcement that Swift became an open-source programming language." }, "topics": [ "Engineering" ], "tags": [ "Open source" ], "related": [ { "id": "the-real-benefits-of-open-why-adjust-lives-by-open-source-programming", "reason": "Compare another company’s explanation of why it develops software openly." }, { "id": "what-to-consider-with-an-open-source-business-model", "reason": "Open-source business models provide context for corporate participation in open source." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/apple-releases-swift-as-open-source", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "how-to-become-an-amazing-contributor-to-an-open-source-project", "slug": "how-to-become-an-amazing-contributor-to-an-open-source-project", "source": { "title": "How to become an amazing contributor (to an open source project)", "type": "article", "canonicalUrl": "https://opensource.com/life/11/3/how-become-amazing-contributor-open-source-project", "authors": [ { "name": "Daniel Doubrovkine", "type": "Person" } ], "publisher": { "name": "Opensource.com", "url": "https://opensource.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://opensource.com/life/11/3/how-become-amazing-contributor-open-source-project", "credit": "Opensource.com, CC BY-SA 3.0", "kind": "source-image", "alt": "Participation lettering over a field and mountain landscape", "sourceWidth": 520, "sourceHeight": 292, "path": "/img/library/how-to-become-an-amazing-contributor-to-an-open-source-project.webp", "width": 520, "height": 292, "thumbnail": { "path": "/img/library/how-to-become-an-amazing-contributor-to-an-open-source-project-small.webp", "width": 480, "height": 270 }, "sourceImage": "https://opensource.com/sites/default/files/lead-images/EDU_participation.png" } }, "summary": "Practical expectations for becoming an effective contributor to an open-source project.", "curation": { "note": null, "listingNote": "Practical expectations for becoming an effective contributor to an open-source project." }, "topics": [ "Engineering" ], "tags": [ "Open source" ], "related": [ { "id": "10-ways-to-contribute-to-an-open-source-project-without-writing-code", "reason": "Expand contribution beyond code changes to other forms of project support." }, { "id": "building-leadership-in-an-open-source-community", "reason": "Community leadership builds on trust and sustained contribution." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/how-to-become-an-amazing-contributor-to-an-open-source-project", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "from-openness-to-transparency-the-role-of-social-media-in-open-source-ecosystems", "slug": "from-openness-to-transparency-the-role-of-social-media-in-open-source-ecosystems", "source": { "title": "From Openness to Transparency: The Role of Social Media in Open Source Ecosystems", "type": "documentation", "canonicalUrl": "https://herbsleb.org/web-pres/slides/OSS-10-6-2011.pdf", "authors": [ { "name": "Jim Herbsleb", "type": "Person" } ], "publisher": { "name": "Carnegie Mellon University", "url": "https://herbsleb.org" }, "datePublished": "2011-10-06", "access": "public", "schemaType": "TechArticle", "preview": { "sourcePage": "https://herbsleb.org/web-pres/slides/OSS-10-6-2011.pdf", "sourceImage": "https://herbsleb.org/web-pres/slides/OSS-10-6-2011.pdf", "credit": "Jim Herbsleb", "kind": "document-page", "alt": "First page of From Openness to Transparency: The Role of Social Media in Open Source Ecosystems.", "sourceWidth": 1800, "sourceHeight": 1350, "path": "/img/library/from-openness-to-transparency-the-role-of-social-media-in-open-source-ecosystems.webp", "width": 1200, "height": 900, "thumbnail": { "path": "/img/library/from-openness-to-transparency-the-role-of-social-media-in-open-source-ecosystems-small.webp", "width": 480, "height": 360 }, "pageNumber": 1 } }, "summary": "A keynote on collaboration, coordination, and visibility in open-source ecosystems.", "curation": { "note": null, "listingNote": "A keynote on collaboration, coordination, and visibility in open-source ecosystems." }, "topics": [ "Engineering" ], "tags": [ "Open source" ], "related": [ { "id": "folding-and-unfolding-balancing-openness-and-transparency-in-open-source-communities", "reason": "Research on protected and visible working spaces deepens the discussion of transparency." }, { "id": "transparency-and-democracy-in-open-source-not-what-you-thought", "reason": "Governance disclosure provides another view of openness in software communities." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/from-openness-to-transparency-the-role-of-social-media-in-open-source-ecosystems", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "the-changing-open-source-landscape", "slug": "the-changing-open-source-landscape", "source": { "title": "The Changing Open Source Landscape", "type": "article", "canonicalUrl": "https://vsoch.github.io/2019/os-thoughts/", "authors": [], "publisher": { "name": "Vanessa", "url": "https://vsoch.github.io" }, "datePublished": "2019-05-24", "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://vsoch.github.io/2019/os-thoughts/", "credit": "Vanessa", "kind": "screenshot", "alt": "Screenshot of The Changing Open Source Landscape", "sourceWidth": 1200, "sourceHeight": 780, "path": "/img/library/the-changing-open-source-landscape.webp", "width": 1200, "height": 780, "thumbnail": { "path": "/img/library/the-changing-open-source-landscape-small.webp", "width": 480, "height": 312 }, "capturedAt": "2026-10-13" } }, "summary": "A reflection on how the incentives and culture of open source have changed.", "curation": { "note": null, "listingNote": "A reflection on how the incentives and culture of open source have changed." }, "topics": [ "Engineering" ], "tags": [ "Open source" ], "related": [ { "id": "what-to-consider-with-an-open-source-business-model", "reason": "Commercial open-source decisions are one part of the changing incentive landscape." }, { "id": "open-source-transparency", "reason": "A complementary discussion examines openness and accountability in communities." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/the-changing-open-source-landscape", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "government-transparency-via-open-data-and-open-source", "slug": "government-transparency-via-open-data-and-open-source", "source": { "title": "Government Transparency via Open Data and Open Source", "type": "article", "canonicalUrl": "https://timreview.ca/article/230", "authors": [ { "name": "Jennifer Bell", "type": "Person" } ], "publisher": { "name": "timreview.ca", "url": "https://timreview.ca" }, "datePublished": null, "access": "public", "schemaType": "Article", "publicationYear": 2009, "preview": { "sourcePage": "https://timreview.ca/article/230", "credit": "TIM Review, Figure 1", "kind": "source-image", "alt": "Traditional three-layer website architecture compared with the Power of Information architecture", "sourceWidth": 648, "sourceHeight": 216, "path": "/img/library/government-transparency-via-open-data-and-open-source.webp", "width": 648, "height": 216, "thumbnail": { "path": "/img/library/government-transparency-via-open-data-and-open-source-small.webp", "width": 480, "height": 160 }, "sourceImage": "https://timreview.ca/ojs/february09/figure1.jpg" } }, "summary": "An argument for building public accountability through open data and open-source software.", "curation": { "note": null, "listingNote": "An argument for building public accountability through open data and open-source software." }, "topics": [ "Engineering" ], "tags": [ "Open source" ], "related": [ { "id": "voting-for-transparency", "reason": "Open-source voting infrastructure provides a concrete public-accountability example." }, { "id": "software-transparency", "reason": "Software transparency addresses another public interest in inspectable systems." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/government-transparency-via-open-data-and-open-source", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "building-leadership-in-an-open-source-community", "slug": "building-leadership-in-an-open-source-community", "source": { "title": "Building Leadership in an Open Source Community", "type": "article", "canonicalUrl": "https://www.linuxfoundation.org/resources/open-source-guides/building-leadership-in-an-open-source-community", "authors": [], "publisher": { "name": "linuxfoundation.org", "url": "https://www.linuxfoundation.org" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://www.linuxfoundation.org/resources/open-source-guides/building-leadership-in-an-open-source-community", "credit": "linuxfoundation.org", "kind": "screenshot", "alt": "Screenshot of Building Leadership in an Open Source Community", "sourceWidth": 1200, "sourceHeight": 500, "path": "/img/library/building-leadership-in-an-open-source-community.webp", "width": 1200, "height": 500, "thumbnail": { "path": "/img/library/building-leadership-in-an-open-source-community-small.webp", "width": 480, "height": 200 }, "capturedAt": "2026-10-13" } }, "summary": "Guidance for developing trust and leadership within open-source communities.", "curation": { "note": null, "listingNote": "Guidance for developing trust and leadership within open-source communities." }, "topics": [ "Engineering" ], "tags": [ "Open source" ], "related": [ { "id": "how-to-become-an-amazing-contributor-to-an-open-source-project", "reason": "Effective contribution provides a foundation for trust and leadership in a community." }, { "id": "a-primer-on-asf-governance-apache-software-foundation", "reason": "Formal governance offers another perspective on how leadership operates." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/building-leadership-in-an-open-source-community", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "voting-for-transparency", "slug": "voting-for-transparency", "source": { "title": "Voting for transparency", "type": "article", "canonicalUrl": "https://increment.com/open-source/voting-for-transparency/", "authors": [], "publisher": { "name": "increment.com", "url": "https://increment.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://increment.com/open-source/voting-for-transparency/", "credit": "increment.com", "kind": "source-image", "alt": "Illustration of a ballot box beside a colorful election-themed pattern", "sourceWidth": 1000, "sourceHeight": 563, "path": "/img/library/voting-for-transparency.webp", "width": 1000, "height": 563, "thumbnail": { "path": "/img/library/voting-for-transparency-small.webp", "width": 480, "height": 270 }, "sourceImage": "https://images.ctfassets.net/3njn2qm7rrbs/4BH31Q6M4RFVCZXxBMSjet/a8cf1759b35e523ba4c95206b78c20b9/cover-2000-17757a68.jpeg?w=1000" } }, "summary": "Reporting on open-source voting infrastructure and public confidence in elections.", "curation": { "note": null, "listingNote": "Reporting on open-source voting infrastructure and public confidence in elections." }, "topics": [ "Engineering" ], "tags": [ "Open source" ], "related": [ { "id": "government-transparency-via-open-data-and-open-source", "reason": "The open-government article develops the public-accountability rationale behind transparent infrastructure." }, { "id": "software-transparency", "reason": "Software transparency provides a technical complement to confidence in voting systems." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/voting-for-transparency", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "10-ways-to-contribute-to-an-open-source-project-without-writing-code", "slug": "10-ways-to-contribute-to-an-open-source-project-without-writing-code", "source": { "title": "10 ways to contribute to an open source project without writing code", "type": "article", "canonicalUrl": "https://opensource.com/life/13/10/ten-ways-open-source-projects", "authors": [ { "name": "Heiko W. Rupp", "type": "Person" } ], "publisher": { "name": "Opensource.com", "url": "https://opensource.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://opensource.com/life/13/10/ten-ways-open-source-projects", "credit": "Opensource.com, CC BY-SA 3.0", "kind": "source-image", "alt": "Hands joined in a circle around the word Trust", "sourceWidth": 520, "sourceHeight": 292, "path": "/img/library/10-ways-to-contribute-to-an-open-source-project-without-writing-code.webp", "width": 520, "height": 292, "thumbnail": { "path": "/img/library/10-ways-to-contribute-to-an-open-source-project-without-writing-code-small.webp", "width": 480, "height": 270 }, "sourceImage": "https://opensource.com/sites/default/files/lead-images/BIZ_HighTrust_1110_A.png" } }, "summary": "Ways to support open-source projects beyond writing code.", "curation": { "note": null, "listingNote": "Ways to support open-source projects beyond writing code." }, "topics": [ "Engineering" ], "tags": [ "Open source" ], "related": [ { "id": "how-to-become-an-amazing-contributor-to-an-open-source-project", "reason": "A contributor guide develops the expectations behind sustained project participation." }, { "id": "building-leadership-in-an-open-source-community", "reason": "Community leadership highlights the value of contribution beyond code." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/10-ways-to-contribute-to-an-open-source-project-without-writing-code", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "ageism-in-tech-the-not-so-invisible-age-limit-developers-face", "slug": "ageism-in-tech-the-not-so-invisible-age-limit-developers-face", "source": { "title": "Ageism in tech: the not-so-invisible age limit developers face", "type": "article", "canonicalUrl": "https://bdtechtalks.com/2019/03/29/ageism-in-tech-age-limit-software-developers-face/", "authors": [], "publisher": { "name": "TechTalks - Technology solving problems... and creating new ones", "url": "https://bdtechtalks.com" }, "datePublished": "2019-03-29", "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://bdtechtalks.com/2019/03/29/ageism-in-tech-age-limit-software-developers-face/", "credit": "TechTalks - Technology solving problems... and creating new ones", "kind": "source-image", "alt": "Man studying a laptop at a wooden table", "sourceWidth": 696, "sourceHeight": 464, "path": "/img/library/ageism-in-tech-the-not-so-invisible-age-limit-developers-face.webp", "width": 696, "height": 464, "thumbnail": { "path": "/img/library/ageism-in-tech-the-not-so-invisible-age-limit-developers-face-small.webp", "width": 480, "height": 320 }, "sourceImage": "https://i0.wp.com/bdtechtalks.com/wp-content/uploads/2019/03/software-developer-too-much-experience-problem.jpg?resize=696%2C464&ssl=1" } }, "summary": "An examination of age-related barriers in software-development careers.", "curation": { "note": null, "listingNote": "An examination of age-related barriers in software-development careers." }, "topics": [ "Engineering" ], "tags": [ "Technical reference" ], "related": [ { "id": "building-leadership-in-an-open-source-community", "reason": "Inclusive participation matters when developing trust and leadership in technical communities." }, { "id": "how-to-become-an-amazing-contributor-to-an-open-source-project", "reason": "Contributor expectations offer a complementary lens on participation in software projects." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/ageism-in-tech-the-not-so-invisible-age-limit-developers-face", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "what-to-consider-with-an-open-source-business-model", "slug": "what-to-consider-with-an-open-source-business-model", "source": { "title": "What to consider with an open source business model", "type": "article", "canonicalUrl": "https://about.gitlab.com/blog/thoughts-on-open-source/", "authors": [], "publisher": { "name": "GitLab", "url": "https://about.gitlab.com" }, "datePublished": "2019-07-05", "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://about.gitlab.com/blog/thoughts-on-open-source/", "credit": "GitLab", "kind": "source-image", "alt": "Winding road through green hills under a blue sky", "sourceWidth": 1275, "sourceHeight": 750, "path": "/img/library/what-to-consider-with-an-open-source-business-model.webp", "width": 1200, "height": 706, "thumbnail": { "path": "/img/library/what-to-consider-with-an-open-source-business-model-small.webp", "width": 480, "height": 282 }, "sourceImage": "https://res.cloudinary.com/about-gitlab-com/image/upload/v1749682919/Blog/Hero%20Images/opensourcecover.jpg" } }, "summary": "A discussion of the decisions behind commercial open-source business models.", "curation": { "note": null, "listingNote": "A discussion of the decisions behind commercial open-source business models." }, "topics": [ "Engineering" ], "tags": [ "Open source" ], "related": [ { "id": "google-we-have-no-moat-and-neither-does-openai", "reason": "The AI competitive-defensibility debate raises related questions about the economics of openness." }, { "id": "the-real-benefits-of-open-why-adjust-lives-by-open-source-programming", "reason": "Adjust supplies a concrete account of corporate open-source practices." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/what-to-consider-with-an-open-source-business-model", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "transparency-and-democracy-in-open-source-not-what-you-thought", "slug": "transparency-and-democracy-in-open-source-not-what-you-thought", "source": { "title": "Transparency and Democracy in open source: not what you thought", "type": "article", "canonicalUrl": "https://livablesoftware.com/transparency-democracy-open-source-not-thought/", "authors": [], "publisher": { "name": "Livable Software", "url": "https://livablesoftware.com" }, "datePublished": "2016-01-15", "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://livablesoftware.com/transparency-democracy-open-source-not-thought/", "credit": "Livable Software", "kind": "source-image", "alt": "Pie chart of open-source projects specifying their governance, with categories for none, limited, and existing", "sourceWidth": 270, "sourceHeight": 330, "path": "/img/library/transparency-and-democracy-in-open-source-not-what-you-thought.webp", "width": 270, "height": 330, "thumbnail": { "path": "/img/library/transparency-and-democracy-in-open-source-not-what-you-thought-small.webp", "width": 270, "height": 330 }, "sourceImage": "https://modeling-languages.com/wp-content/uploads/2016/01/governance.png" } }, "summary": "An examination of how open-source projects disclose their governance and decision-making.", "curation": { "note": null, "listingNote": "An examination of how open-source projects disclose their governance and decision-making." }, "topics": [ "Engineering" ], "tags": [ "Open source" ], "related": [ { "id": "opengovernance-opengovernance-dev", "reason": "The checklist makes governance-transparency questions concrete." }, { "id": "a-primer-on-asf-governance-apache-software-foundation", "reason": "Apache provides a formal governance model to compare with those questions." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/transparency-and-democracy-in-open-source-not-what-you-thought", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "folding-and-unfolding-balancing-openness-and-transparency-in-open-source-communities", "slug": "folding-and-unfolding-balancing-openness-and-transparency-in-open-source-communities", "source": { "title": "Folding and Unfolding: Balancing Openness and Transparency in Open Source Communities", "type": "paper", "canonicalUrl": "https://doi.org/10.1287/isre.2016.0646", "authors": [ { "name": "Maha Shaikh", "type": "Person" }, { "name": "Emmanuelle Vaast", "type": "Person" } ], "publisher": { "name": "Institute for Operations Research and the Management Sciences (INFORMS)", "url": "https://doi.org" }, "datePublished": null, "access": "public", "schemaType": "ScholarlyArticle", "publicationYear": 2016, "doi": "10.1287/isre.2016.0646", "preview": { "sourcePage": "https://wrap.warwick.ac.uk/78627/", "sourceImage": "https://wrap.warwick.ac.uk/id/eprint/78627/7/WRAP_WBS-ISRDigitalFold-ShaikhVaast%20%2528004%2529.pdf", "credit": "Maha Shaikh, Emmanuelle Vaast", "kind": "document-page", "alt": "Title page of Folding and Unfolding: Balancing Openness and Transparency in Open Source Communities.", "sourceWidth": 1272, "sourceHeight": 1800, "path": "/img/library/folding-and-unfolding-balancing-openness-and-transparency-in-open-source-communities.webp", "width": 1131, "height": 1600, "thumbnail": { "path": "/img/library/folding-and-unfolding-balancing-openness-and-transparency-in-open-source-communities-small.webp", "width": 452, "height": 640 }, "pageNumber": 2, "caption": "Title page of the accepted manuscript, preserved by the University of Warwick." } }, "summary": "Research on how open-source developers balance visible collaboration with protected working spaces.", "curation": { "note": null, "listingNote": "Research on how open-source developers balance visible collaboration with protected working spaces." }, "topics": [ "Engineering" ], "tags": [ "Open source" ], "related": [ { "id": "from-openness-to-transparency-the-role-of-social-media-in-open-source-ecosystems", "reason": "The keynote examines the role of visibility in open-source collaboration." }, { "id": "how-working-transparently-united-our-engineers", "reason": "The Buffer account provides a practical example of working more transparently." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/folding-and-unfolding-balancing-openness-and-transparency-in-open-source-communities", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "how-working-transparently-united-our-engineers", "slug": "how-working-transparently-united-our-engineers", "source": { "title": "How working transparently united our engineers", "type": "article", "canonicalUrl": "https://opensource.com/open-organization/17/10/transparency-open-source-buffer", "authors": [ { "name": "Jordan Morgan", "type": "Person" } ], "publisher": { "name": "Opensource.com", "url": "https://opensource.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://opensource.com/open-organization/17/10/transparency-open-source-buffer", "credit": "Opensource.com, CC BY-SA 4.0", "kind": "source-image", "alt": "A mosaic of colorful interlocking shapes", "sourceWidth": 520, "sourceHeight": 292, "path": "/img/library/how-working-transparently-united-our-engineers.webp", "width": 520, "height": 292, "thumbnail": { "path": "/img/library/how-working-transparently-united-our-engineers-small.webp", "width": 480, "height": 270 }, "sourceImage": "https://opensource.com/sites/default/files/lead-images/open_abstract_pieces.jpg" } }, "summary": "An account of how transparency helped engineers at Buffer collaborate.", "curation": { "note": null, "listingNote": "An account of how transparency helped engineers at Buffer collaborate." }, "topics": [ "Engineering" ], "tags": [ "Open source" ], "related": [ { "id": "folding-and-unfolding-balancing-openness-and-transparency-in-open-source-communities", "reason": "The research study explores tensions between visible collaboration and protected working spaces." }, { "id": "open-source-and-transparency-not-the-same-thing", "reason": "Distinguishing open code from transparent operations clarifies what changed in the team." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/how-working-transparently-united-our-engineers", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "open-source-and-transparency-not-the-same-thing", "slug": "open-source-and-transparency-not-the-same-thing", "source": { "title": "Open Source and Transparency: Not The Same Thing", "type": "article", "canonicalUrl": "https://mautic.org/blog/open-source-and-transparency-not-the-same-thing/", "authors": [], "publisher": { "name": "Mautic", "url": "https://mautic.org" }, "datePublished": "2014-12-22", "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://mautic.org/blog/open-source-and-transparency-not-the-same-thing/", "credit": "Mautic", "kind": "screenshot", "alt": "Screenshot of Open Source and Transparency: Not The Same Thing", "sourceWidth": 1200, "sourceHeight": 780, "path": "/img/library/open-source-and-transparency-not-the-same-thing.webp", "width": 1200, "height": 780, "thumbnail": { "path": "/img/library/open-source-and-transparency-not-the-same-thing-small.webp", "width": 480, "height": 312 }, "capturedAt": "2026-10-13" } }, "summary": "An explanation of the distinction between sharing source code and operating transparently.", "curation": { "note": null, "listingNote": "An explanation of the distinction between sharing source code and operating transparently." }, "topics": [ "Engineering" ], "tags": [ "Open source" ], "related": [ { "id": "transparency-and-democracy-in-open-source-not-what-you-thought", "reason": "Governance disclosure illustrates transparency beyond source-code availability." }, { "id": "opengovernance-opengovernance-dev", "reason": "The checklist offers concrete questions about ownership and decision-making." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/open-source-and-transparency-not-the-same-thing", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "a-primer-on-asf-governance-apache-software-foundation", "slug": "a-primer-on-asf-governance-apache-software-foundation", "source": { "title": "A Primer on ASF Governance | Apache Software Foundation", "type": "article", "canonicalUrl": "https://www.apache.org/foundation/governance/", "authors": [], "publisher": { "name": "apache.org", "url": "https://www.apache.org" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://www.apache.org/foundation/governance/", "credit": "apache.org", "kind": "screenshot", "alt": "Screenshot of A Primer on ASF Governance | Apache Software Foundation", "sourceWidth": 1200, "sourceHeight": 780, "path": "/img/library/a-primer-on-asf-governance-apache-software-foundation.webp", "width": 1200, "height": 780, "thumbnail": { "path": "/img/library/a-primer-on-asf-governance-apache-software-foundation-small.webp", "width": 480, "height": 312 }, "capturedAt": "2026-10-13" } }, "summary": "The Apache Software Foundation’s corporate governance structure and procedures.", "curation": { "note": null, "listingNote": "The Apache Software Foundation’s corporate governance structure and procedures." }, "topics": [ "Engineering" ], "tags": [ "Open source" ], "related": [ { "id": "bylaws-linux-foundation", "reason": "Compare Apache’s governance with another foundation’s formal bylaws." }, { "id": "opengovernance-opengovernance-dev", "reason": "The checklist helps examine ownership and decision-making structures." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/a-primer-on-asf-governance-apache-software-foundation", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "open-source-transparency", "slug": "open-source-transparency", "source": { "title": "Open Source Transparency", "type": "article", "canonicalUrl": "https://vsoch.github.io/2019/transparency/", "authors": [], "publisher": { "name": "Vanessa", "url": "https://vsoch.github.io" }, "datePublished": "2019-03-17", "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://vsoch.github.io/2019/transparency/", "credit": "Vanessa", "kind": "screenshot", "alt": "Screenshot of Open Source Transparency", "sourceWidth": 1200, "sourceHeight": 780, "path": "/img/library/open-source-transparency.webp", "width": 1200, "height": 780, "thumbnail": { "path": "/img/library/open-source-transparency-small.webp", "width": 480, "height": 312 }, "capturedAt": "2026-10-13" } }, "summary": "A discussion of incentives, openness, and accountability in software communities.", "curation": { "note": null, "listingNote": "A discussion of incentives, openness, and accountability in software communities." }, "topics": [ "Engineering" ], "tags": [ "Open source" ], "related": [ { "id": "transparency-and-democracy-in-open-source-not-what-you-thought", "reason": "Governance transparency makes the accountability discussion more concrete." }, { "id": "folding-and-unfolding-balancing-openness-and-transparency-in-open-source-communities", "reason": "Research examines how developers balance openness with protected working spaces." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/open-source-transparency", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "opengovernance-opengovernance-dev", "slug": "opengovernance-opengovernance-dev", "source": { "title": "opengovernance/opengovernance.dev", "type": "repository", "canonicalUrl": "https://github.com/opengovernance/opengovernance.dev", "authors": [], "publisher": { "name": "GitHub", "url": "https://github.com" }, "datePublished": null, "access": "public", "schemaType": "SoftwareSourceCode", "preview": { "sourcePage": "https://github.com/opengovernance/opengovernance.dev", "credit": "Open Governance contributors / GitHub", "kind": "screenshot", "capturedAt": "2026-10-13", "alt": "Open Governance repository checklist covering project ownership and decision-making.", "sourceWidth": 838, "sourceHeight": 528, "path": "/img/library/opengovernance-opengovernance-dev.webp", "width": 838, "height": 528, "thumbnail": { "path": "/img/library/opengovernance-opengovernance-dev-small.webp", "width": 480, "height": 302 } } }, "summary": "A checklist for evaluating ownership and decision-making in open-source governance.", "curation": { "note": null, "listingNote": "A checklist for evaluating ownership and decision-making in open-source governance." }, "topics": [ "AI & machine learning", "Engineering" ], "tags": [ "Evaluation", "Open source" ], "related": [ { "id": "open-container-initiative-charter", "reason": "Apply the governance questions to the Open Container Initiative charter." }, { "id": "a-primer-on-asf-governance-apache-software-foundation", "reason": "Apache’s governance primer supplies another concrete organizational model." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/opengovernance-opengovernance-dev", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "open-source-security-through-transparency-iot-now-news-reports", "slug": "open-source-security-through-transparency-iot-now-news-reports", "source": { "title": "Open source: Security through transparency | IoT Now News & Reports", "type": "article", "canonicalUrl": "https://iot-now.com/2015/12/14/39527-open-source-security-through-transparency/", "authors": [ { "name": "Zenobia Hegde", "type": "Person" } ], "publisher": { "name": "IoT Now News - How to run an IoT enabled business", "url": "https://iot-now.com" }, "datePublished": "2015-12-14", "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://iot-now.com/2015/12/14/39527-open-source-security-through-transparency/", "credit": "IoT Now News - How to run an IoT enabled business", "kind": "screenshot", "alt": "Screenshot of Open source: Security through transparency | IoT Now News & Reports", "sourceWidth": 1200, "sourceHeight": 780, "path": "/img/library/open-source-security-through-transparency-iot-now-news-reports.webp", "width": 1200, "height": 780, "thumbnail": { "path": "/img/library/open-source-security-through-transparency-iot-now-news-reports-small.webp", "width": 480, "height": 312 }, "capturedAt": "2026-10-13" } }, "summary": "A discussion of the security implications of transparent software development.", "curation": { "note": null, "listingNote": "A discussion of the security implications of transparent software development." }, "topics": [ "Engineering", "Security & defense" ], "tags": [ "Open source", "Security" ], "related": [ { "id": "software-transparency", "reason": "A complementary security argument examines transparency as a defense against hidden vulnerabilities." }, { "id": "government-transparency-via-open-data-and-open-source", "reason": "Open-government accountability provides another motivation for inspectable software." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/open-source-security-through-transparency-iot-now-news-reports", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "software-transparency", "slug": "software-transparency", "source": { "title": "Software Transparency", "type": "article", "canonicalUrl": "https://blog.citp.princeton.edu/2013/09/16/software-transparency/", "authors": [ { "name": "Ed Felten", "type": "Person" } ], "publisher": { "name": "CITP Blog", "url": "https://blog.citp.princeton.edu" }, "datePublished": "2013-09-16", "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://blog.citp.princeton.edu/2013/09/16/software-transparency/", "credit": "CITP Blog", "kind": "screenshot", "alt": "Screenshot of Software Transparency", "sourceWidth": 1200, "sourceHeight": 780, "path": "/img/library/software-transparency.webp", "width": 1200, "height": 780, "thumbnail": { "path": "/img/library/software-transparency-small.webp", "width": 480, "height": 312 }, "capturedAt": "2026-10-13" } }, "summary": "An examination of software transparency as a defense against hidden vulnerabilities.", "curation": { "note": null, "listingNote": "An examination of software transparency as a defense against hidden vulnerabilities." }, "topics": [ "Engineering", "Security & defense" ], "tags": [ "Open source", "Security" ], "related": [ { "id": "open-source-security-through-transparency-iot-now-news-reports", "reason": "Compare another account of the security consequences of software transparency." }, { "id": "voting-for-transparency", "reason": "Voting infrastructure provides a high-consequence application of public trust in software." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/software-transparency", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "the-real-benefits-of-open-why-adjust-lives-by-open-source-programming", "slug": "the-real-benefits-of-open-why-adjust-lives-by-open-source-programming", "source": { "title": "The real benefits of open: Why Adjust lives by open-source programming", "type": "article", "canonicalUrl": "https://www.adjust.com/blog/the-benefits-of-open-source/", "authors": [], "publisher": { "name": "Adjust", "url": "https://www.adjust.com" }, "datePublished": null, "access": "public", "schemaType": "Article", "availability": "unavailable", "preview": { "sourcePage": "https://web.archive.org/web/20220125145654/https://www.adjust.com/blog/the-benefits-of-open-source/", "sourceImage": "https://web.archive.org/web/20200323041917im_/https://a.storyblok.com/f/47007/72573c4e87/the-benefits-of-open-source-card-image.png", "credit": "Adjust", "kind": "source-image", "alt": "Adjust’s original open-source article artwork, with its logo over a background of binary digits.", "sourceWidth": 1000, "sourceHeight": 500, "path": "/img/library/the-real-benefits-of-open-why-adjust-lives-by-open-source-programming.webp", "width": 1000, "height": 500, "thumbnail": { "path": "/img/library/the-real-benefits-of-open-why-adjust-lives-by-open-source-programming-small.webp", "width": 480, "height": 240 } } }, "summary": "Adjust explains the reasoning behind its open-source software and SDK practices.", "curation": { "note": null, "listingNote": "Adjust explains the reasoning behind its open-source software and SDK practices." }, "topics": [ "Engineering" ], "tags": [ "Open source" ], "related": [ { "id": "what-to-consider-with-an-open-source-business-model", "reason": "The business-model discussion helps interpret a company’s reasons for publishing software openly." }, { "id": "apple-releases-swift-as-open-source", "reason": "Compare another corporate decision to release software as open source." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/the-real-benefits-of-open-why-adjust-lives-by-open-source-programming", "status": "curated", "citedIn": [ { "title": "TensorFlow World 2019: A Case for Open Source", "url": "https://fpaupier.fr/201910_tensorflow_world" } ] }, { "id": "european-chips-survey", "slug": "european-chips-survey", "source": { "title": "European Chips Survey", "type": "article", "canonicalUrl": "https://digital-strategy.ec.europa.eu/en/library/european-chips-survey", "authors": [], "publisher": { "name": "Shaping Europe’s digital future", "url": "https://digital-strategy.ec.europa.eu" }, "datePublished": null, "access": "public", "schemaType": "Article", "preview": { "sourcePage": "https://digital-strategy.ec.europa.eu/en/library/european-chips-survey", "credit": "Shaping Europe’s digital future", "kind": "source-image", "alt": "European Chips Act illustration with the message No digital without chips", "sourceWidth": 480, "sourceHeight": 270, "path": "/img/library/european-chips-survey.webp", "width": 480, "height": 270, "thumbnail": { "path": "/img/library/european-chips-survey-small.webp", "width": 480, "height": 270 }, "sourceImage": "https://digital-strategy.ec.europa.eu/sites/default/files/styles/newsroom_large/public/newsroom/items/Chips_Act_1_169_centred_H0VEFwyTY2h8AbdliUeosXQoiUM_101938.jpg?itok=BoLkzi0g" } }, "summary": "The European Commission’s survey of semiconductor demand and supply-chain constraints.", "curation": { "note": null, "listingNote": "The European Commission’s survey of semiconductor demand and supply-chain constraints." }, "topics": [ "Engineering" ], "tags": [ "Semiconductors" ], "related": [ { "id": "european-chips-act", "reason": "The European Chips Act supplies the policy setting for semiconductor constraints." }, { "id": "semiconductor-resilience", "reason": "A global supply-chain report broadens the survey’s European perspective." } ], "archive": { "snapshotUrl": null }, "detailUrl": "https://fpaupier.fr/library/european-chips-survey", "status": "curated", "citedIn": [ { "title": "LLM : souveraineté et supply chains", "url": "https://fpaupier.fr/202509_genai_ecn" } ] }, { "id": "la-strategie-indopacifique-de-la-france", "slug": "la-strategie-indopacifique-de-la-france", "source": { "title": "La stratégie indopacifique de la France", "type": "report", "canonicalUrl": "https://www.diplomatie.gouv.fr/files/files/priorites-et-actions/grands-dossiers/strate_gie_indopacifique_de_la_france_2025_cle0bc96b.pdf", "authors": [], "publisher": { "name": "Ministère de l’Europe et des Affaires étrangères", "url": "https://www.diplomatie.gouv.fr" }, "datePublished": null, "access": "public", "schemaType": "Report", "publicationYear": 2025, "preview": { "sourcePage": "https://www.diplomatie.gouv.fr/files/files/priorites-et-actions/grands-dossiers/strate_gie_indopacifique_de_la_france_2025_cle0bc96b.pdf", "sourceImage": "https://www.diplomatie.gouv.fr/files/files/priorites-et-actions/grands-dossiers/strate_gie_indopacifique_de_la_france_2025_cle0bc96b.pdf", "credit": "Ministère de l’Europe et des Affaires étrangères", "kind": "document-page", "alt": "First page of La stratégie indopacifique de la France.", "sourceWidth": 1273, "sourceHeight": 1800, "path": "/img/library/la-strategie-indopacifique-de-la-france.webp", "width": 1132, "height": 1600, "thumbnail": { "path": "/img/library/la-strategie-indopacifique-de-la-france-small.webp", "width": 453, "height": 640 }, "pageNumber": 1 } }, "summary": "France’s strategic priorities in the Indo-Pacific, including security, regional cooperation, and economic dependencies.", "curation": { "note": null, "listingNote": "France’s strategic priorities in the Indo-Pacific, including security, regional cooperation, and economic dependencies." }, "topics": [ "Company building", "Engineering" ], "tags": [ "Economics", "Semiconductors", "Security" ], "related": [ { 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